A Markov random field model for combining optimum-path forest classifiers using decision graphs and game strategy approach

The research on multiple classifiers systems includes the creation of an ensemble of classifiers and the proper combination of the decisions. In order to combine the decisions given by classifiers, methods related to fixed rules and decision templates are often used. Therefore, the influence and relationship between classifier decisions are often not considered in the combination schemes. In this paper we propose a framework to combine classifiers using a decision graph under a random field model and a game strategy approach to obtain the final decision. The results of combining Optimum-Path Forest (OPF) classifiers using the proposed model are reported, obtaining good performance in experiments using simulated and real data sets. The results encourage the combination of OPF ensembles and the framework to design multiple classifier systems. © 2011 Springer-Verlag.

Multiscale Fractal Descriptors and Polynomial Classifier for Partial Pixels Identification in Regions of Interest of Mammographic Images

Computer systems are used to support breast cancer diagnosis, with decisions taken from measurements carried out in regions of interest (ROIs). We show that support decisions obtained from square or rectangular ROIs can to include background regions with different behavior of healthy or diseased tissues. In this study, the background regions were identified as Partial Pixels (PP), obtained with a multilevel method of segmentation based on maximum entropy. The behaviors of healthy, diseased and partial tissues were quantified by fractal dimension and multiscale lacunarity, calculated through signatures of textures. The separability of groups was achieved using a polynomial classifier. The polynomials have powerful approximation properties as classifiers to treat characteristics linearly separable or not. This proposed method allowed quantifying the ROIs investigated and demonstrated that different behaviors are obtained, with distinctions of 90% for images obtained in the Cranio-caudal (CC) and Mediolateral Oblique (MLO) views.

Brazilian Students and Working Capital: An Analysis of Their Decisions When Using Virtual Market Business Games

This study aims to demonstrate that data from business games can be an important resource for improving efficiency and effectiveness of learning. The proposal presented here was developed from preliminary studies of data from Virtual Market games that pointed the possibility of identifying gaps in learning by analyzing the decisions of students. This proposal helps students to refine their learning processes and equips tutors with strategies for teaching and student assessment. The proposal also complements the group discussion and/or debriefing, which are widely used to enhance learning mediated by games. However, from a management perspective the model has the potential to be erroneous and miss opportunities, which cannot be detected because of the dependence on the characteristics of the individual, such as ability to communicate and work together. To illustrate the proposed technique, data sets from two business games were analyzed with the focus on managing working capital and it was found that students had difficulties managing this task. Similar trends were observed in all categories of students in the study-undergraduate, postgraduate and specialization. This discovery led us to the analysis of data for decisions made in the performance of the games, and it was determined that indicators could be developed that were capable of indentifying inconsistencies in the decisions. It was decided to apply some basic concepts of the finance management, such as management of the operational and non-operational expenditures, as well as production management concepts, such as the use of the production capacity. By analyzing the data from the Virtual Market games using the indicator concept, it was possible to detect the lack of domain knowledge of the students. Therefore, these indicators can be used to analyze the decisions of the players and guide them during the game, increasing their effectiveness and efficiency. As these indicators were developed from specific content, they can also be used to develop teaching materials to support learning. Viewed in this light, the proposal adds new possibilities for using business games in learning. In addition to the intrinsic learning that is achieved through playing the games, they also assist in driving the learning process. This study considers the applications and the methodology used.

Wear Particle Classifier System Based on an Artificial Neural Network

This paper describes a method of identifying morphological attributes that classify wear particles in relation to the wear process from which they originate and permit the automatic identification without human expertise. The method is based on the use of Multi Layer Perceptron (MLP) for analysis of specific types of microscopic wear particles. The classification of the wear particles was performed according to their morphological attributes of size and aspect ratio, among others. (C) 2010 Journal of Mechanical Engineering. All rights reserved.

Comparison of visual inspection, radiographic examination, laser fluorescence and their combinations on treatment decisions for occlusal surfaces

Purpose: To compare visual inspection (VI), radiographic examination (RX) and the laser fluorescence device DIAGNOdent (L), as well as their combinations in vitro regarding treatment decisions for occlusal surfaces. Methods: 72 extracted human permanent teeth (molars and premolars) were used. Treatment decisions were recorded by three calibrated examiners, and the options available were fissure sealant and conservative restoration. For validation of treatment decisions, the teeth were sectioned and examined in a stereomicroscope. Thereafter, dental slices were scanned and the images were edited to facilitate classification of existing carious lesions. Intra and inter-examiner reproducibility for the determination of treatment plans were calculated using Cohen's kappa test (95%-CI). Sensitivity, specificity, positive and negative predictive values, and the area under the ROC curve were also calculated. Results: VI and L provided on average the greatest intra- and inter-examiner reproducibility, respectively. Although the combination of diagnostic methods may decrease both intra- and inter examiners reproducibility, combination of VI, L and RX resulted in the greatest sensitivity, being statistically superior to RX and L. There was more inter-examiner agreement for the option of restorative treatment, while the use of sealants as a treatment option yielded the lowest values. Negative predictive values were numerically inferior to positive predictive values, indicating that the examiners preferred not to restore a carious tooth than to proceed operatively in an intact tooth. The combination of the three methods studied showed the best results in determining treatment plans for occlusal surfaces, when compared to the other types of exams. on the other hand, radiographic examination and laser fluorescence were less efficient when used alone.

When decisions on homologous structures cause ambiguous taxa relationships: the Neomorphinae (Aves, Cuculidae) example

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Optimum-Path Forest Classifier for Large Scale Biometric Applications

This paper addresses biometric identification using large databases, in particular, iris databases. In such applications, it is critical to have low response time, while maintaining an acceptable recognition rate. Thus, the trade-off between speed and accuracy must be evaluated for processing and recognition parts of an identification system. In this paper, a graph-based framework for pattern recognition, called Optimum-Path Forest (OPF), is utilized as a classifier in a pre-developed iris recognition system. The aim of this paper is to verify the effectiveness of OPF in the field of iris recognition, and its performance for various scale iris databases. The existing Gauss-Laguerre Wavelet based coding scheme is used for iris encoding. The performance of the OPF and two other - Hamming and Bayesian - classifiers, is compared using small, medium, and large-scale databases. Such a comparison shows that the OPF has faster response for large-scale databases, thus performing better than the more accurate, but slower, classifiers.

In Vitro Evaluation of ICDAS and Radiographic Examination of Occlusal Surfaces and Their Association With Treatment Decisions

This in vitro study evaluated the performance of visual (International Caries Detection and Assessment System [ICDAS]) and radiographic (bitewing [BW]) examinations for occlusal caries detection and their associations with treatment decision (TD). Permanent teeth (n=104) with occlusal surfaces varying from sound to cavitated were selected. Sites were identified from 10x occlusal surface photographs. Standardized bitewing (BW) radiographs were taken. Four dentists with at least five years of experience scored all teeth twice (one-week interval) for ICDAS (0-6), BW (0=sound, 1=caries restricted to enamel, 2=caries in outer third dentin, 3=caries in inner third dentin), and TD (0=no treatment, 1=sealant, 2=microabrasion and sealant, 3=round bur sealant, 4a=resin, 4b=amalgam). Histological validation was performed by observation under a light microscope, with lesions classified on a five-point scale. Intraexaminer and inter-examiner repeatability were assessed using two-way tables and intraclass correlation coefficients (ICCs). Comparisons between percentage correct, specificity, sensitivity, and area under the receiver-operating characteristic (ROC) curve were performed using bootstrap analyses. ICCs for intraexaminer and interexaminer repeatability indicated good repeatability for each examiner, ranging from 0.78 to 0.88, and among examiners, ranging from 0.74 to 0.81. Correlation between ICDAS and TD was 0.85 and between BW and TD was 0.78. Correlation between the methods and histological scores was moderate (0.63 for ICDAS and 0.61 for BW). The area under the ROC curve was significantly greater for ICDAS than for BW (p<0.0001). ICDAS had significantly lower specificity than BW did (p=0.0269, 79% vs 94%); however, sensitivity was much higher for ICDAS than for BW (p<0.0001, 83% vs 44%). Data from this investigation suggested that the visual examination (ICDAS) showed better performance than radiographic examination for occlusal caries detection. The ICDAS was strongly associated with TD. Although the correlation between the ICDAS and BW was lower, it is still valuable in the clinical decision-making process.

Hybrid visual-driven decision support system in video monitor manufacturing

An intelligent system that emulates human decision behaviour based on visual data acquisition is proposed. The approach is useful in applications where images are used to supply information to specialists who will choose suitable actions. An artificial neural classifier aids a fuzzy decision support system to deal with uncertainty and imprecision present in available information. Advantages of both techniques are exploited complementarily. As an example, this method was applied in automatic focus checking and adjustment in video monitor manufacturing. Copyright © 2005 IFAC.

Improving the accuracy of the optimum-path forest supervised classifier for large datasets

In this work, a new approach for supervised pattern recognition is presented which improves the learning algorithm of the Optimum-Path Forest classifier (OPF), centered on detection and elimination of outliers in the training set. Identification of outliers is based on a penalty computed for each sample in the training set from the corresponding number of imputable false positive and false negative classification of samples. This approach enhances the accuracy of OPF while still gaining in classification time, at the expense of a slight increase in training time. © 2010 Springer-Verlag.

Improving accuracy and speed of optimum-path forest classifier using combination of disjoint training subsets

The Optimum-Path Forest (OPF) classifier is a recent and promising method for pattern recognition, with a fast training algorithm and good accuracy results. Therefore, the investigation of a combining method for this kind of classifier can be important for many applications. In this paper we report a fast method to combine OPF-based classifiers trained with disjoint training subsets. Given a fixed number of subsets, the algorithm chooses random samples, without replacement, from the original training set. Each subset accuracy is improved by a learning procedure. The final decision is given by majority vote. Experiments with simulated and real data sets showed that the proposed combining method is more efficient and effective than naive approach provided some conditions. It was also showed that OPF training step runs faster for a series of small subsets than for the whole training set. The combining scheme was also designed to support parallel or distributed processing, speeding up the procedure even more. © 2011 Springer-Verlag.

A snoring classifier based on heart rate variability analysis

The effect of snoring on the cardiovascular system is not well-known. In this study we analyzed the Heart Rate Variability (HRV) differences between light and heavy snorers. The experiments are done on the full-whole-night polysomnography (PSG) with ECG and audio channels from patient group (heavy snorer) and control group (light snorer), which are gender- and age-paired, totally 30 subjects. A feature Snoring Density (SND) of audio signal as classification criterion and HRV features are computed. Mann-Whitney statistical test and Support Vector Machine (SVM) classification are done to see the correlation. The result of this study shows that snoring has close impact on the HRV features. This result can provide a deeper insight into the physiological understand of snoring. © 2011 CCAL.

Strategies to model scheduling decisions to plan the soft drink production process

In this paper we present a mixed integer model that integrates lot sizing and lot scheduling decisions for the production planning of a soft drink company. The main contribution of the paper is to present a model that differ from others in the literature for the constraints related to the scheduling decisions. The proposed strategy is compared to other strategies presented in the literature.

Model of a game for improving integrated decisions in production management

This paper presents the results obtained with a business game whose model represents the decision making process related to two moments at an industrial company. The first refers to the project of the industrial plant, and the second to its management. The game model was conceived so the player's first decision would establish capacity and other parameters such as quantities of each product to produce, marketing expenses, research and development, quality, advertising, salaries, if purchases will be made in installments or in cash, if there will be credit sales and how many installments will be allowed and the number of workers in the assembly area. An experiment was conducted with employees of a Brazilian company. Data obtained indicate that the players have lack of contents, especially in finances. Although these results cannot be generalized, they confirm prior results with undergraduate and graduate students and they indicate the need for reinforcement in this undergraduate area. © 2012 Springer-Verlag.

Classification of masses in mammographic image using wavelet domain features and polynomial classifier

Breast cancer is the most common cancer among women. In CAD systems, several studies have investigated the use of wavelet transform as a multiresolution analysis tool for texture analysis and could be interpreted as inputs to a classifier. In classification, polynomial classifier has been used due to the advantages of providing only one model for optimal separation of classes and to consider this as the solution of the problem. In this paper, a system is proposed for texture analysis and classification of lesions in mammographic images. Multiresolution analysis features were extracted from the region of interest of a given image. These features were computed based on three different wavelet functions, Daubechies 8, Symlet 8 and bi-orthogonal 3.7. For classification, we used the polynomial classification algorithm to define the mammogram images as normal or abnormal. We also made a comparison with other artificial intelligence algorithms (Decision Tree, SVM, K-NN). A Receiver Operating Characteristics (ROC) curve is used to evaluate the performance of the proposed system. Our system is evaluated using 360 digitized mammograms from DDSM database and the result shows that the algorithm has an area under the ROC curve Az of 0.98 ± 0.03. The performance of the polynomial classifier has proved to be better in comparison to other classification algorithms. © 2013 Elsevier Ltd. All rights reserved.