Induction by a Hilbert hypercube representation

This thesis describes a novel connectionist machine utilizing induction by a Hilbert hypercube representation. This representation offers a number of distinct advantages which are described. We construct a theoretical and practical learning machine which lies in an area of overlap between three disciplines - neural nets, machine learning and knowledge acquisition - hence it is refered to as a "coalesced" machine. To this unifying aspect is added the various advantages of its orthogonal lattice structure as against less structured nets. We discuss the case for such a fundamental and low level empirical learning tool and the assumptions behind the machine are clearly outlined. Our theory of an orthogonal lattice structure the Hilbert hypercube of an n-dimensional space using a complemented distributed lattice as a basis for supervised learning is derived from first principles on clearly laid out scientific principles. The resulting "subhypercube theory" was implemented in a development machine which was then used to test the theoretical predictions again under strict scientific guidelines. The scope, advantages and limitations of this machine were tested in a series of experiments. Novel and seminal properties of the machine include: the "metrical", deterministic and global nature of its search; complete convergence invariably producing minimum polynomial solutions for both disjuncts and conjuncts even with moderate levels of noise present; a learning engine which is mathematically analysable in depth based upon the "complexity range" of the function concerned; a strong bias towards the simplest possible globally (rather than locally) derived "balanced" explanation of the data; the ability to cope with variables in the network; and new ways of reducing the exponential explosion. Performance issues were addressed and comparative studies with other learning machines indicates that our novel approach has definite value and should be further researched.

Caractérisation de minéraux indicateurs par imagerie hyperspectrale et traitement de l'image dans l'infrarouge proche et l'infrarouge lointain

L’imagerie hyperspectrale (HSI) fournit de l’information spatiale et spectrale concernant l’émissivité de la surface des matériaux, ce qui peut être utilisée pour l’identification des minéraux. Pour cela, un matériel de référence ou endmember, qui en minéralogie est la forme la plus pure d’un minéral, est nécessaire. L’objectif principal de ce projet est l’identification des minéraux par imagerie hyperspectrale. Les informations de l’imagerie hyperspectrale ont été enregistrées à partir de l’énergie réfléchie de la surface du minéral. L’énergie solaire est la source d’énergie dans l’imagerie hyperspectrale de télédétection, alors qu’un élément chauffant est la source d’énergie utilisée dans les expériences de laboratoire. Dans la première étape de ce travail, les signatures spectrales des minéraux purs sont obtenues avec la caméra hyperspectrale, qui mesure le rayonnement réfléchi par la surface des minéraux. Dans ce projet, deux séries d’expériences ont été menées dans différentes plages de longueurs d’onde (0,4 à 1 µm et 7,7 à 11,8 µm). Dans la deuxième partie de ce projet, les signatures spectrales obtenues des échantillons individuels sont comparées avec des signatures spectrales de la bibliothèque hyperspectrale de l’ASTER. Dans la troisième partie, trois méthodes différentes de classification hyperspectrale sont considérées pour la classification. Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), et Intercorrélation normalisée (NCC). Enfin, un système d’apprentissage automatique, Extreme Learning Machine (ELM), est utilisé pour identifier les minéraux. Deux types d’échantillons ont été utilisés dans ce projet. Le système d’ELM est divisé en deux parties, la phase d’entraînement et la phase de test du système. Dans la phase d’entraînement, la signature d’un seul échantillon minéral est entrée dans le système, et dans la phase du test, les signatures spectrales des différents minéraux, qui sont entrées dans la phase d’entraînement, sont comparées par rapport à des échantillons de minéraux mixtes afin de les identifier.

Hybrid model for the training of interval type-2 fuzzy logic system

In this paper, a hybrid training model for interval type-2 fuzzy logic system is proposed. The hybrid training model uses extreme learning machine to tune the consequent part parameters and genetic algorithm to optimize the antecedent part parameters. The proposed hybrid learning model of interval type-2 fuzzy logic system is tested on the prediction of Mackey-Glass time series data sets with different levels of noise. The results are compared with the existing models in literature; extreme learning machine and Kalman filter based learning of consequent part parameters with randomly generated antecedent part parameters. It is observed that the interval type-2 fuzzy logic system provides improved performance with the proposed hybrid learning model.

Um estudo de descritores e classificadores para reconhecimento de bagas de café.

RESUMO - Métodos de reconhecimento de frutos baseados na utilização de diferentes descritores e classificadores foram estudados. Foi utilizada uma base de dados de 3.393 imagens de café e não-café anteriormente criada e rotulada manualmente. Testes quantitativos demonstraram a identificação de bagas com 93% de precisão e 77% de cobertura utilizando descritores HoG adicionados a mediana dos componentes de cor do formato La*b*, aliados ao classificador Gradient Boosting. Esses resultados melhoram o método anteriormente proposto por Santos (2015), e demonstram a possibilidade de evolução de métodos que podem ser aplicados em metodologias de agricultura de precisão, monitoramento e predição de safra.

Empirical comparison of machine learning algorithms for image texture classification with application to vegetation management in power line corridors

This paper reports on the empirical comparison of seven machine learning algorithms in texture classification with application to vegetation management in power line corridors. Aiming at classifying tree species in power line corridors, object-based method is employed. Individual tree crowns are segmented as the basic classification units and three classic texture features are extracted as the input to the classification algorithms. Several widely used performance metrics are used to evaluate the classification algorithms. The experimental results demonstrate that the classification performance depends on the performance matrix, the characteristics of datasets and the feature used.

Application of machine learning in fault diagnostics of mechanical systems

A diagnostic method based on Bayesian Networks (probabilistic graphical models) is presented. Unlike conventional diagnostic approaches, in this method instead of focusing on system residuals at one or a few operating points, diagnosis is done by analyzing system behavior patterns over a window of operation. It is shown how this approach can loosen the dependency of diagnostic methods on precise system modeling while maintaining the desired characteristics of fault detection and diagnosis (FDD) tools (fault isolation, robustness, adaptability, and scalability) at a satisfactory level. As an example, the method is applied to fault diagnosis in HVAC systems, an area with considerable modeling and sensor network constraints.

Evidence for CRHR1 in multiple sclerosis using supervised machine learning and meta-analysis in 12 566 individuals

The primary genetic risk factor in multiple sclerosis (MS) is the HLA-DRB1*1501 allele; however, much of the remaining genetic contribution to MS has yet to be elucidated. Several lines of evidence support a role for neuroendocrine system involvement in autoimmunity which may, in part, be genetically determined. Here, we comprehensively investigated variation within eight candidate hypothalamic-pituitary-adrenal (HPA) axis genes and susceptibility to MS. A total of 326 SNPs were investigated in a discovery dataset of 1343 MS cases and 1379 healthy controls of European ancestry using a multi-analytical strategy. Random Forests, a supervised machine-learning algorithm, identified eight intronic SNPs within the corticotrophin-releasing hormone receptor 1 or CRHR1 locus on 17q21.31 as important predictors of MS. On the basis of univariate analyses, six CRHR1 variants were associated with decreased risk for disease following a conservative correction for multiple tests. Independent replication was observed for CRHR1 in a large meta-analysis comprising 2624 MS cases and 7220 healthy controls of European ancestry. Results from a combined meta-analysis of all 3967 MS cases and 8599 controls provide strong evidence for the involvement of CRHR1 in MS. The strongest association was observed for rs242936 (OR = 0.82, 95% CI = 0.74-0.90, P = 9.7 × 10-5). Replicated CRHR1 variants appear to exist on a single associated haplotype. Further investigation of mechanisms involved in HPA axis regulation and response to stress in MS pathogenesis is warranted. © The Author 2010. Published by Oxford University Press. All rights reserved.

A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Automatic classification of free-text radiology reports to identify limb fractures using machine learning and the SNOMED CT ontology

Objective To develop and evaluate machine learning techniques that identify limb fractures and other abnormalities (e.g. dislocations) from radiology reports. Materials and Methods 99 free-text reports of limb radiology examinations were acquired from an Australian public hospital. Two clinicians were employed to identify fractures and abnormalities from the reports; a third senior clinician resolved disagreements. These assessors found that, of the 99 reports, 48 referred to fractures or abnormalities of limb structures. Automated methods were then used to extract features from these reports that could be useful for their automatic classification. The Naive Bayes classification algorithm and two implementations of the support vector machine algorithm were formally evaluated using cross-fold validation over the 99 reports. Result Results show that the Naive Bayes classifier accurately identifies fractures and other abnormalities from the radiology reports. These results were achieved when extracting stemmed token bigram and negation features, as well as using these features in combination with SNOMED CT concepts related to abnormalities and disorders. The latter feature has not been used in previous works that attempted classifying free-text radiology reports. Discussion Automated classification methods have proven effective at identifying fractures and other abnormalities from radiology reports (F-Measure up to 92.31%). Key to the success of these techniques are features such as stemmed token bigrams, negations, and SNOMED CT concepts associated with morphologic abnormalities and disorders. Conclusion This investigation shows early promising results and future work will further validate and strengthen the proposed approaches.

Classification of cancer-related death certificates using machine learning

Background Cancer monitoring and prevention relies on the critical aspect of timely notification of cancer cases. However, the abstraction and classification of cancer from the free-text of pathology reports and other relevant documents, such as death certificates, exist as complex and time-consuming activities. Aims In this paper, approaches for the automatic detection of notifiable cancer cases as the cause of death from free-text death certificates supplied to Cancer Registries are investigated. Method A number of machine learning classifiers were studied. Features were extracted using natural language techniques and the Medtex toolkit. The numerous features encompassed stemmed words, bi-grams, and concepts from the SNOMED CT medical terminology. The baseline consisted of a keyword spotter using keywords extracted from the long description of ICD-10 cancer related codes. Results Death certificates with notifiable cancer listed as the cause of death can be effectively identified with the methods studied in this paper. A Support Vector Machine (SVM) classifier achieved best performance with an overall F-measure of 0.9866 when evaluated on a set of 5,000 free-text death certificates using the token stem feature set. The SNOMED CT concept plus token stem feature set reached the lowest variance (0.0032) and false negative rate (0.0297) while achieving an F-measure of 0.9864. The SVM classifier accounts for the first 18 of the top 40 evaluated runs, and entails the most robust classifier with a variance of 0.001141, half the variance of the other classifiers. Conclusion The selection of features significantly produced the most influences on the performance of the classifiers, although the type of classifier employed also affects performance. In contrast, the feature weighting schema created a negligible effect on performance. Specifically, it is found that stemmed tokens with or without SNOMED CT concepts create the most effective feature when combined with an SVM classifier.