Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems

Nakashima, Tomoharu; Yokota, Yasuyuki; Ishibuchi, Hisao; Schaefer, Gerald; Drastich, Aleš; Zavisek, M.

doi:10.20965/jaciii.2007.p0546

Cited by 8 publications

(5 citation statements)

References 15 publications

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“…Thus, the pattern space into the n -dimensional pattern classification problem uses unit hypercube [0, 1] n [ 5 , 6 , 34 ]. For an n -dimensional, c -class problem, we apply fuzzy if-then rule of the following form:

where R j is the label of the i th fuzzy if-then rule, N is the total number of fuzzy if-then rules, X = [ x 1 ,…, x n ] is the pattern vector n -dimensional, A j 1 presents antecedent fuzzy sets for the i th attribute, C j represent a consequent class (i.e., one of the c classes), and CF j is a certainty grade of the fuzzy if-then R j [ 2 , 5 , 9 , 13 , 15 , 34 , 35 ].…”

Section: Methods: Weighting Fuzzy Classification Systemmentioning

confidence: 99%

Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease

Tahamtan

Abedi

Bagheri

et al. 2015

Computational and Mathematical Methods in Medicine

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The aim of this study was to determine the accuracy of fuzzy rule-based classification that could noninvasively predict CAD based on myocardial perfusion scan test and clinical-epidemiological variables. This was a cross-sectional study in which the characteristics, the results of myocardial perfusion scan (MPS), and coronary artery angiography of 115 patients, 62 (53.9%) males, in Mazandaran Heart Center in the north of Iran have been collected. We used membership functions for medical variables by reviewing the related literature. To improve the classification performance, we used Ishibuchi et al. and Nozaki et al. methods by adjusting the grade of certainty CF j of each rule. This system includes 144 rules and the antecedent part of all rules has more than one part. The coronary artery disease data used in this paper contained 115 samples. The data was classified into four classes, namely, classes 1 (normal), 2 (stenosis in one single vessel), 3 (stenosis in two vessels), and 4 (stenosis in three vessels) which had 39, 35, 17, and 24 subjects, respectively. The accuracy in the fuzzy classification based on if-then rule was 92.8 percent if classification result was considered based on rule selection by expert, while it was 91.9 when classification result was obtained according to the equation. To increase the classification rate, we deleted the extra rules to reduce the fuzzy rules after introducing the membership functions.

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Section: Methods: Weighting Fuzzy Classification Systemmentioning

confidence: 99%

Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease

Tahamtan

Abedi

Bagheri

et al. 2015

Computational and Mathematical Methods in Medicine

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“…A different approach to address class imbalance is cost-sensitive classification which is based on the idea of assigning a cost to misclassifications [ 20 ]. Conventional classifiers try to reduce the number of misclassifications but do not pay attention to which class they belong.…”

Section: Imbalanced Classificationmentioning

confidence: 99%

Imbalanced Ensemble Learning for Enhanced Pulsar Identification

Holewik

Schaefer

Korovin

2020

Lecture Notes in Computer Science

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Pulsars can be detected based on their emitted radio waves. Machine learning methods can be employed to support automated screening of a large number of radio signals for pulsars. This is however a challenging task since training these methods is affected by an inherent imbalance in the acquired data with signals relating to actual pulsars being in the minority. In this paper, we demonstrate that ensemble classification methods that are dedicated to imbalanced classification problems can be successfully employed for pulsar identification. Classifier ensembles combine several individual classifiers to yield more robust and improved classification, while class imbalance can be addressed through careful sampling or through cost-sensitive classification. Experimental results, based on HTRU2 data, show that the investigated ensembles outperform methods that do not consider class balance, and suggest their use for other applications in astrophysics.

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“…One possibility is to perform one-class classification, which can learn the concepts of the minority class by treating majority class objects as outliers [15]. Cost-sensitive approaches can use both data modifications (by adding a specified cost to the misclassification) and modifications of the learning algorithms (to adapt them to the possibility of misclassification) [23]. Here, a higher misclassification cost is assigned for minority class objects and classification performed so as to reduce the overall learning cost.…”

Section: Imbalanced Classificationmentioning

confidence: 99%

An ensemble classification approach for melanoma diagnosis

et al. 2014

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Malignant melanoma is the deadliest form of skin cancer, and has, among cancer types, one of the most rapidly increasing incidence rates in the world. Early diagnosis is crucial, since if detected early, its cure is simple. In this paper, we present an effective approach to melanoma identification from dermoscopic images of skin lesions based on ensemble classification. First, we perform automatic border detection to segment the lesion from the background skin. Based on the extracted border, we extract a series of colour, texture and shape features. The derived features are then employed in a pattern classification stage for which we employ a novel, dedicated ensemble learning approach to address the class imbalance in the training data and to yield improved classification performance. Our classifier committee trains individual classifiers on balanced subspaces, removes redundant predictors based on a diversity measure and combines the remaining classifiers using a neural network fuser. Experimental results on a large dataset of dermoscopic skin lesion images show our approach to work well, to provide both high sensitivity and specificity, and our presented classifier ensemble to lead to statistically better

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Constructing Cost-Sensitive Fuzzy-Rule-Based Systems for Pattern Classification Problems

Cited by 8 publications

References 15 publications

Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease

Fuzzy Rule-Based Classification System for Assessing Coronary Artery Disease

Imbalanced Ensemble Learning for Enhanced Pulsar Identification

An ensemble classification approach for melanoma diagnosis

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