A measure of competence based on random classification for dynamic ensemble selection

Woloszynski, Tomasz; Kurzyński, Marek; Podsiadło, Paweł; Stachowiak, Gwidon

doi:10.1016/j.inffus.2011.03.007

Cited by 118 publications

(65 citation statements)

References 30 publications

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“…It was shown that the heterogeneous classifiers perform well for 22 benchmark data sets. 27,31 Selection of accurate classifiers and combination of the classifiers selected. To select accurate classifiers from the diverse classifier ensemble, the MCR method was used.…”

Section: Dmc Systemmentioning

confidence: 99%

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A dissimilarity-based multiple classifier system for trabecular bone texture in detection and prediction of progression of knee osteoarthritis

Woloszynski

Podsiadło

Stachowiak

et al. 2012

Proc Inst Mech Eng H

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There is a growing need for classification systems that can accurately detect and predict knee osteoarthritis (OA) from plain radiographs. For this purpose, a system based on a support vector machine (SVM) classifier and distances measured between trabecular bone (TB) texture images was developed and tested in previous work. Unlike other systems, it allows an image classification without the calculation and selection of numerous texture features, and it is invariant to a range of imaging conditions encountered in a routine X-ray screening of knees. Although the system exhibited 85.4% classification accuracy in OA detection, which was higher than those obtained from other systems, its performance could be further improved. To achieve this, a dissimilarity-based multiple classifier (DMC) system is developed in this study. The system measures distances between TB texture images and generates a diverse ensemble of classifiers using prototype selection, bootstrapping of training set and heterogeneous classifiers. A measure of competence is used to select accurate (i.e. better-than-random) classifiers from the ensemble, which are then combined through the majority voting rule. To evaluate the newly developed system in OA detection (prediction of OA progression), TB texture images selected on standardised radiographs of healthy and OA (non-progressive and progressive OA) knees were used. The results obtained showed that the DMC system has higher classification accuracies for the detection (90.51% with 87.65% specificity and 93.33% sensitivity) and prediction (80% with 82.00% specificity and 77.97% sensitivity) than other systems, indicating its potential as a decision-support tool for the assessment of radiographic knee OA.

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Section: Dmc Systemmentioning

confidence: 99%

“…To select accurate classifiers from the diverse classifier ensemble, the MCR method was used. 31 For each TB texture image, the method estimates local classification accuracies of all classifiers. Accuracies of heterogeneous classifiers are estimated using weighted k = 10 nearest neighbours.…”

Section: Dmc Systemmentioning

confidence: 99%

A dissimilarity-based multiple classifier system for trabecular bone texture in detection and prediction of progression of knee osteoarthritis

Woloszynski

Podsiadło

Stachowiak

et al. 2012

Proc Inst Mech Eng H

Self Cite

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“…For the calculation of the competence, various performance estimates are used, such as local accuracy estimation (Didaci et al, 2005), the Bayes confidence measure (Huenupán et al, 2008), multiple classifier behaviour (Giacinto and Roli, 2001), the oracle based measure (Ko et al, 2008), methods based on relating that of the classifier with the response obtained by random guessing (Woloszynski et al, 2012) or the randomized classification model (Woloszynski and Kurzynski, 2011), among others.…”

Section: Introductionmentioning

confidence: 99%

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Trajdos

Kurzyński

2016

International Journal of Applied Mathematics and Computer Science

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Nowadays, multiclassifier systems (MCSs) are being widely applied in various machine learning problems and in many different domains. Over the last two decades, a variety of ensemble systems have been developed, but there is still room for improvement. This paper focuses on developing competence and interclass cross-competence measures which can be applied as a method for classifiers combination. The cross-competence measure allows an ensemble to harness pieces of information obtained from incompetent classifiers instead of removing them from the ensemble. The cross-competence measure originally determined on the basis of a validation set (static mode) can be further easily updated using additional feedback information on correct/incorrect classification during the recognition process (dynamic mode). The analysis of computational and storage complexity of the proposed method is presented. The performance of the MCS with the proposed cross-competence function was experimentally compared against five reference MCSs and one reference MCS for static and dynamic modes, respectively. Results for the static mode show that the proposed technique is comparable with the reference methods in terms of classification accuracy. For the dynamic mode, the system developed achieves the highest classification accuracy, demonstrating the potential of the MCS for practical applications when feedback information is available.

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“…Recently, methods for ensemble selection have received much attention in the literature (e.g., Niculescu-Mizil et al, 2009;Partalas et al, 2010;Woloszynski et al, 2012). Such techniques add an additional modeling stage to the ensemble learning process.…”

Section: Heterogeneous Ensemble Classifiersmentioning

confidence: 99%

Data mining techniques: Modern approaches to application in credit scoring

Volkova¹,

Гисин²,

Solov'ev³

2017

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state-of-the-art in credit scoring. In addition, we repeat our benchmark for several performance measures and examine the degree to which these differ in their assessment of candidate scorecards. Our study provides valuable insight for professionals and academics in credit scoring.The paper helps practitioners to stay abreast of technical advancements in predictive modeling.From an academic standpoint, the study provides an independent assessment of recent scoring methods and offers a new baseline to which future approaches can be compared.

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A measure of competence based on random classification for dynamic ensemble selection

Cited by 118 publications

References 30 publications

A dissimilarity-based multiple classifier system for trabecular bone texture in detection and prediction of progression of knee osteoarthritis

A dissimilarity-based multiple classifier system for trabecular bone texture in detection and prediction of progression of knee osteoarthritis

A dynamic model of classifier competence based on the local fuzzy confusion matrix and the random reference classifier

Data mining techniques: Modern approaches to application in credit scoring

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