Machine recognition of hand-drawn circuit diagrams

An application of image processing techniques to recognition of hand-drawn circuit diagrams is presented. The scanned image of a diagram is pre-processed to remove noise and converted to bilevel. Morphological operations are applied to obtain a clean, connected representation using thinned lines. The diagram comprises of nodes, connections and components. Nodes and components are segmented using appropriate thresholds on a spatially varying object pixel density. Connection paths are traced using a pixel-stack. Nodes are classified using syntactic analysis. Components are classified using a combination of invariant moments, scalar pixel-distribution features, and vector relationships between straight lines in polygonal representations. A node recognition accuracy of 82% and a component recognition accuracy of 86% was achieved on a database comprising 107 nodes and 449 components. This recogniser can be used for layout “beautification” or to generate input code for circuit analysis and simulation packages

Higher order spectra based support vector machine for arrhythmia classification

Heart rate variability (HRV) refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. HRV analysis is an important tool to observe the heart’s ability to respond to normal regulatory impulses that affect its rhythm. Like many bio-signals, HRV signals are non-linear in nature. Higher order spectral analysis (HOS) is known to be a good tool for the analysis of non-linear systems and provides good noise immunity. A computer-based arrhythmia detection system of cardiac states is very useful in diagnostics and disease management. In this work, we studied the identification of the HRV signals using features derived from HOS. These features were fed to the support vector machine (SVM) for classification. Our proposed system can classify the normal and other four classes of arrhythmia with an average accuracy of more than 85%.

A model for the classification and survey of clock synchronization protocols in WSNs

Clock synchronization in wireless sensor networks (WSNs) assures that sensor nodes have the same reference clock time. This is necessary not only for various WSN applications but also for many system level protocols for WSNs such as MAC protocols, and protocols for sleep scheduling of sensor nodes. Clock value of a node at a particular instant of time depends on its initial value and the frequency of the crystal oscillator used in the sensor node. The frequency of the crystal oscillator varies from node to node, and may also change over time depending upon many factors like temperature, humidity, etc. As a result, clock values of different sensor nodes diverge from each other and also from the real time clock, and hence, there is a requirement for clock synchronization in WSNs. Consequently, many clock synchronization protocols for WSNs have been proposed in the recent past. These protocols differ from each other considerably, and so, there is a need to understand them using a common platform. Towards this goal, this survey paper categorizes the features of clock synchronization protocols for WSNs into three types, viz, structural features, technical features, and global objective features. Each of these categories has different options to further segregate the features for better understanding. The features of clock synchronization protocols that have been used in this survey include all the features which have been used in existing surveys as well as new features such as how the clock value is propagated, when the clock value is propagated, and when the physical clock is updated, which are required for better understanding of the clock synchronization protocols in WSNs in a systematic way. This paper also gives a brief description of a few basic clock synchronization protocols for WSNs, and shows how these protocols fit into the above classification criteria. In addition, the recent clock synchronization protocols for WSNs, which are based on the above basic clock synchronization protocols, are also given alongside the corresponding basic clock synchronization protocols. Indeed, the proposed model for characterizing the clock synchronization protocols in WSNs can be used not only for analyzing the existing protocols but also for designing new clock synchronization protocols. (C) 2014 Elsevier B.V. All rights reserved.

Computerised Diagnostic Decision Support System for the Classification of Pre-Invasive Cervical Squamous Lesions.

Previous studies have revealed considerable interobserver and intraobserver variation in the histological classification of preinvasive cervical squamous lesions. The aim of the present study was to develop a decision support system (DSS) for the histological interpretation of these lesions. Knowledge and uncertainty were represented in the form of a Bayesian belief network that permitted the storage of diagnostic knowledge and, for a given case, the collection of evidence in a cumulative manner that provided a final probability for the possible diagnostic outcomes. The network comprised 8 diagnostic histological features (evidence nodes) that were each independently linked to the diagnosis (decision node) by a conditional probability matrix. Diagnostic outcomes comprised normal; koilocytosis; and cervical intraepithelial neoplasia (CIN) 1, CIN II, and CIN M. For each evidence feature, a set of images was recorded that represented the full spectrum of change for that feature. The system was designed to be interactive in that the histopathologist was prompted to enter evidence into the network via a specifically designed graphical user interface (i-Path Diagnostics, Belfast, Northern Ireland). Membership functions were used to derive the relative likelihoods for the alternative feature outcomes, the likelihood vector was entered into the network, and the updated diagnostic belief was computed for the diagnostic outcomes and displayed. A cumulative probability graph was generated throughout the diagnostic process and presented on screen. The network was tested on 50 cervical colposcopic biopsy specimens, comprising 10 cases each of normal, koilocytosis, CIN 1, CIN H, and CIN III. These had been preselected by a consultant gynecological pathologist. Using conventional morphological assessment, the cases were classified on 2 separate occasions by 2 consultant and 2 junior pathologists. The cases were also then classified using the DSS on 2 occasions by the 4 pathologists and by 2 medical students with no experience in cervical histology. Interobserver and intraobserver agreement using morphology and using the DSS was calculated with K statistics. Intraobserver reproducibility using conventional unaided diagnosis was reasonably good (kappa range, 0.688 to 0.861), but interobserver agreement was poor (kappa range, 0.347 to 0.747). Using the DSS improved overall reproducibility between individuals. Using the DSS, however, did not enhance the diagnostic performance of junior pathologists when comparing their DSS-based diagnosis against an experienced consultant. However, the generation of a cumulative probability graph also allowed a comparison of individual performance, how individual features were assessed in the same case, and how this contributed to diagnostic disagreement between individuals. Diagnostic features such as nuclear pleomorphism were shown to be particularly problematic and poorly reproducible. DSSs such as this therefore not only have a role to play in enhancing decision making but also in the study of diagnostic protocol, education, self-assessment, and quality control. (C) 2003 Elsevier Inc. All rights reserved.

Concentric characterization and classification of complex network nodes: Application to an institutional collaboration network

Differently from theoretical scale-free networks, most real networks present multi-scale behavior, with nodes structured in different types of functional groups and communities. While the majority of approaches for classification of nodes in a complex network has relied on local measurements of the topology/connectivity around each node, valuable information about node functionality can be obtained by concentric (or hierarchical) measurements. This paper extends previous methodologies based on concentric measurements, by studying the possibility of using agglomerative clustering methods, in order to obtain a set of functional groups of nodes, considering particular institutional collaboration network nodes, including various known communities (departments of the University of Sao Paulo). Among the interesting obtained findings, we emphasize the scale-free nature of the network obtained, as well as identification of different patterns of authorship emerging from different areas (e.g. human and exact sciences). Another interesting result concerns the relatively uniform distribution of hubs along concentric levels, contrariwise to the non-uniform pattern found in theoretical scale-free networks such as the BA model. (C) 2008 Elsevier B.V. All rights reserved.

Lymphedema after complete axillary node dissection for melanoma: assessment using a new, objective definition

Objectives: The objectives of this study were to define appropriate criteria for assessing the presence of lymphedema, and to report the prevalence and risk factors for development of upper limb lymphedema after level I-III axillary dissection for melanoma.
Summary Background Data: The lack of a consistent and reliable objective definition for lymphedema remains a significant barrier to appreciating its prevalence after axillary dissection for melanoma (or breast carcinoma).
Methods: Lymphedema was assessed in 107 patients (82 male, 25 female) who had previously undergone complete level I-III axillary dissection. Of the 107 patients, 17 had also received postoperative axillary radiotherapy. Arm volume was measured using a water displacement technique. Change in volume of the arm on the side of the dissection was referenced to the volume of the other (control) arm. Volume measurements were corrected for the effect of handedness using corrections derived from a control group. Classification and regression tree (CART) analysis was used to determine a threshold fractional arm volume increase above which volume changes were considered to indicate lymphedema.
Results: Based on the CART analysis results, lymphedema was defined as an increase in arm volume greater than 16% of the volume of the control arm. Using this definition, lymphedema prevalence for patients in the present study was 10% after complete level I-III axillary dissection for melanoma and 53% after additional axillary radiotherapy. Radiotherapy and wound complications were independent risk factors for the development of lymphedema.
Conclusions: A suggested objective definition for arm lymphedema after axillary dissection is an arm volume increase of greater than 16% of the volume of the control arm.

Two-way regionalized classification of multivariate datasets and its application to the assessment of hydrodynamic dispersion

Zones of mixing between shallow groundwaters of different composition were unravelled by two-way regionalized classification, a technique based on correspondence analysis (CA), cluster analysis (ClA) and discriminant analysis (DA), aided by gridding, map-overlay and contouring tools. The shallow groundwaters are from a granitoid plutonite in the Funda o region (central Portugal). Correspondence analysis detected three natural clusters in the working dataset: 1, weathering; 2, domestic effluents; 3, fertilizers. Cluster analysis set an alternative distribution of the samples by the three clusters. Group memberships obtained by correspondence analysis and by cluster analysis were optimized by discriminant analysis, gridded memberships as follows: codes 1, 2 or 3 were used when classification by correspondence analysis and cluster analysis produced the same results; code 0 when the grid node was first assigned to cluster 1 and then to cluster 2 or vice versa (mixing between weathering and effluents); code 4 in the other cases (mixing between agriculture and the other influences). Code-3 areas were systematically surrounded by code-4 areas, an observation attributed to hydrodynamic dispersion. Accordingly, the extent of code-4 areas in two orthogonal directions was assumed proportional to the longitudinal and transverse dispersivities of local soils. The results (0.7-16.8 and 0.4-4.3 m, respectively) are acceptable at the macroscopic scale. The ratios between longitudinal and transverse dispersivities (1.2-11.1) are also in agreement with results obtained by other studies.

Clinicopathological features influencing pelvic lymph node metastasis and vaginal and parametrial involvement in patients with carcinoma of the cervix

Purpose: This study was undertaken to evaluate clinical and pathologic findings that predicted pelvic lymph node metastasis and parametrial and vaginal involvement in patients with stage IB carcinoma of the cervix. Methods: 71 patients with diagnosis of stage IB (FIGO) cervical cancer were prospectively studied from December 1997 to August 2002. The patient's age, clinical stage (IB1 or IB2), histological classification, grade of differentiation, tumor volume, and lymphatic vascular space invasion (LVSI) were evaluated. Statistical methods included chi(2) test and Fisher's exact test to evaluate significant differences between the groups. The level of significance was set at p < 0.05. Results: the clinical stage was IB1 in 51 patients (71.8%) and IB2 in 20 patients (28.2%). The histological classification identified squamous cell carcinoma in 60 patients (84.5%) and adenocarcinoma in 11 patients (15.5%). The average tumoral volume was 22.8 &PLUSMN; 8 24.3 cm(3) (0.3-140.0 cm(3)). The tumor was well differentiated (G1) in 8 (11.3%), moderately differentiated (G2) in 40 (56.3%) and poorly differentiated in 23 (32.4%) of the cases. The presence of LVSI was detected in 14 patients (19.7%) and was associated with pelvic lymph node metastasis and vaginal and parametrial involvement (p = 0.002, p = 0.001 and p < 0.001; respectively). The average number of positive pelvic lymph nodes was significantly higher in the patients with LVSI compared with patients without LVSI (2.47 +/- 2.8 vs. 0.33 +/- 0.74; p = 0.001). There was no association of age, clinical stage, histological classification, grade of differentiation or tumor volume with pelvic lymph node metastasis and vaginal and parametrial involvement. Conclusion: the presence of LVSI is significantly associated with pelvic lymph node metastasis and vaginal and parametrial involvement in patients with stage IB cervical carcinoma. Copyright (C) 2005 S. Karger AG, Basel.

A new approach to soil classification mapping based on the spatial distribution of soil properties

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Topoisomerase expression in oral squamous cell carcinoma: relationship with cancer stem cells profiles and lymph node metastasis

BACKGROUND: The relationship between predictive proteins and tumors presenting cancer stem cells (CSCs) profiles in oral tumors is still poorly understood. This study aims to identify the relationship between topoisomerases I, II alpha, and III alpha and putative CSCs immunophenotype in oral squamous cell carcinoma (OSCC) and determine its influence on prognosis. METHODS: The following data were retrieved from 127 patients: age, gender, primary anatomic site, smoking and alcohol intake, recurrence, metastases, histologic classification, treatment, and survival. An immunohistochemical study for topoisomerases I, II alpha, and III alpha was performed in a tissue microarray containing 127 paraffin blocks of OSCCs. RESULTS: In univariate analysis, topoisomerases expression showed significant differences according to CSCs profiles and p53 immunoexpression, but not with survival. Topoisomerases II alpha and III alpha also showed significant relationship with lymph node metastasis. The multivariate test confirmed these associations. CONCLUSIONS: The results that all topoisomerases correlates with OSCC CSCs may indicate a role for topoisomerases in head and neck carcinogenesis. Notwithstanding, it is plausible that other members of topoisomerases family could represent novel therapeutical targets in oral squamous cell carcinoma. J Oral Pathol Med (2012) 41: 762-768

Four-gene expression model predictive of lymph node metastases in oral squamous cell carcinoma

Background. Previous knowledge of cervical lymph node compromise may be crucial to choose the best treatment strategy in oral squamous cell carcinoma (OSCC). Here we propose a set four genes, whose mRNA expression in the primary tumor predicts nodal status in OSCC, excluding tongue. Material and methods. We identified differentially expressed genes in OSCC with and without compromised lymph nodes using Differential Display RT-PCR. Known genes were chosen to be validated by means of Northern blotting or real time RT-PCR (qRT-PCR). Thereafter we constructed a Nodal Index (NI) using discriminant analysis in a learning set of 35 patients, which was further validated in a second independent group of 20 patients. Results. Of the 63 differentially expressed known genes identified comparing three lymph node positive (pN+) and three negative (pN0) primary tumors, 23 were analyzed by Northern analysis or RT-PCR in 49 primary tumors. Six genes confirmed as differentially expressed were used to construct a NI, as the best set predictive of lymph nodal status, with the final result including four genes. The NI was able to correctly classify 32 of 35 patients comprising the learning group (88.6%; p = 0.009). Casein kinase 1alpha1 and scavenger receptor class B, member 2 were found to be up regulated in pN + group in contrast to small proline-rich protein 2B and Ras-GTPase activating protein SH3 domain-binding protein 2 which were upregulated in the pN0 group. We validated further our NI in an independent set of 20 primary tumors, 11 of them pN0 and nine pN+ with an accuracy of 80.0% (p = 0.012). Conclusions. The NI was an independent predictor of compromised lymph nodes, taking into the consideration tumor size and histological grade. The genes identified here that integrate our "Nodal Index" model are predictive of lymph node metastasis in OSCC.

Prognostic factors for lymph node metastasis from advanced squamous cell carcinoma of the skin of the trunk and extremities

Abstract Background Squamous cell carcinoma (SCC) of the skin of the trunk and extremities may present lymph node metastasis with difficult disease control and poor survival. The purpose of this study was to identify risk factors for lymph node metastasis and outcome. Patients/Methods Retrospective review of 57 patients with locally advanced SCC of the trunk and extremities was performed and several clinical variables including age, gender, ethnicity, previously injured skin (burns, scars, ulcers and others), patient origin (rural or urban), anatomic site and treatment were studied. Results Fifteen patients presented with previous skin lesions. Thirty-six were classified as T3 tumors and 21 as T4; 46 were N0, and 11, N1. Eleven N0 patients presented lymph node metastasis during follow up. Univariate analysis identified previous skin lesions (ulcers and scars) as risk factor for lymph node metastasis (p = 0.047). Better survival was demonstrated for T3 (p = 0.018) classification. N0 patients who presented lymph node metastasis during follow up (submitted to lymphadenectomy) had similar survival to patients without lymph node recurrence (p = 0.219). Conclusion Local advanced tumors are at risk of lymph node metastasis. Increased risk is associated to previous lesions at tumor site. T4 classification have worse prognosis. Lymph node recurrences in N0 patients, once treated, did not affect survival. For these patients, we propose close follow up and prompt treatment of lymph node metastasis. These results do not support indication for elective lymphadenectomy or sentinel node mapping. Further prospective studies must address this issue.

Survival in surgically treated, nodal positive prostate cancer patients is predicted by histopathological characteristics of the primary tumor and its lymph node metastases

BACKGROUND: Histopathological risk factors for survival stratification of surgically treated nodal positive prostate cancer patients are poorly defined as reflected by only one category for nodal metastases. METHODS: We evaluated biochemical recurrence-free survival (RFS), disease-specific survival (DSS), and overall survival (OS) in 102 nodal positive, hormone treatment-naïve prostate cancer patients (median age: 65 years, range: 45-75 years; median follow-up 7.7 years, range: 1.0-15.9 years) who underwent radical prostatectomy and standardized extended lymphadenectomy. RESULTS: A significant stratification was possible, with the Gleason score of the primary and virtually all nodal parameters favoring patients with better differentiated primaries and metastases, lower nodal tumor burden, and without extranodal extension of metastases. In multivariate analyses, diameter of the largest metastasis (< or =10 mm vs. >10 mm) was the strongest independent predictor for RFS (P < 0.001), DSS (P < 0.001), and OS (P < 0.001) with a more than quadrupled relative risk of cancer related deaths for patients with larger metastases (Hazard ratio: 4.2, Confidence interval: 2.0-8.9; 5-year RFS/DSS/OS: 18%/57%/54%). The highest 5-year survival rates were seen in patients with micrometastases only (RFS/DSS/OS: 47%/94%/94%). CONCLUSION: The TNM classification's current allocation of only one category for nodal metastases in prostate cancers is unsatisfactory since subgroups with significantly different prognoses can be identified. The diameter of the patient's largest metastasis (< or =10 mm vs. >10 mm) should be used for substaging because of its independent prognostic value. The substage "micrometastasis only" is also useful in nodal positive prostate cancer since it designates the subgroup with the most favorable outcome.

Prognostic implications of the seventh edition of the international union against cancer classification for patients with gastric cancer: the Western experience of patients treated in a single-center European institution

PURPOSE Validity of the seventh edition of the American Joint Committee on Cancer/International Union Against Cancer (AJCC/UICC) staging systems for gastric cancer has been evaluated in several studies, mostly in Asian patient populations. Only few data are available on the prognostic implications of the new classification system on a Western population. Therefore, we investigated its prognostic ability based on a German patient cohort. PATIENTS AND METHODS Data from a single-center cohort of 1,767 consecutive patients surgically treated for gastric cancer were classified according to the seventh edition and were compared using the previous TNM/UICC classification. Kaplan-Meier analyses were performed for all TNM stages and UICC stages in a comparative manner. Additional survival receiver operating characteristic analyses and bootstrap-based goodness-of-fit comparisons via Bayesian information criterion (BIC) were performed to assess and compare prognostic performance of the competing classification systems. RESULTS We identified the UICC pT/pN stages according to the seventh edition of the AJCC/UICC guidelines as well as resection status, age, Lauren histotype, lymph-node ratio, and tumor grade as independent prognostic factors in gastric cancer, which is consistent with data from previous Asian studies. Overall survival rates according to the new edition were significantly different for each individual's pT, pN, and UICC stage. However, BIC analysis revealed that, owing to higher complexity, the new staging system might not significantly alter predictability for overall survival compared with the old system within the analyzed cohort from a statistical point of view. CONCLUSION The seventh edition of the AJCC/UICC classification was found to be valid with distinctive prognosis for each stage. However, the AJCC/UICC classification has become more complex without improving predictability for overall survival in a Western population. Therefore, simplification with better predictability of overall survival of patients with gastric cancer should be considered when revising the seventh edition.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.