Classifier selection for majority voting

Ruta, Dymitr; Gabrys, Bogdan

doi:10.1016/j.inffus.2004.04.008

Cited by 542 publications

(330 citation statements)

References 32 publications

(71 reference statements)

Supporting

Mentioning

315

Contrasting

Unclassified

Order By: Relevance

“…In the current work we integrate FURIA-based fuzzy MCSs within the OCS strategy. Since there are many optimization criteria considered for MCS design such as accuracy, complexity, and diversity measures [1,25,26,27], the use of a EMO algorithm came naturally to our mind.…”

Section: Introductionmentioning

confidence: 99%

A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

Trawiński¹,

Cordón²,

Quirin³

2012

IJCIS

View full text Add to dashboard Cite

In a preceding contribution, we conducted a study considering a fuzzy multiclassifier system (MCS) design framework based on Fuzzy Unordered Rule Induction Algorithm (FURIA). It served as the fuzzy rule classification learning algorithm to derive the component classifiers considering bagging and feature selection. In this work, we integrate this approach under the overproduce-and-choose strategy. A state-ofthe-art evolutionary multiobjective algorithm, namely NSGA-II, is used to provide a component classifier selection and improve FURIA-based fuzzy MCS. We propose five different fitness functions based on three different optimization criteria, accuracy, complexity, and diversity. Twenty UCI high dimensional datasets were considered in order to conduct the experiments. A combination between accuracy and diversity criteria provided very promising results, becoming competitive with classical MCS learning methods.

show abstract

Section: Introductionmentioning

confidence: 99%

A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

Trawiński¹,

Cordón²,

Quirin³

2012

IJCIS

View full text Add to dashboard Cite

show abstract

“…In this study we analyse the use of statistical re-sampling theory [7,9,10,12] in generation of PCA ensembles as a way of reducing or removing the influence of outliers on the generated principal components as well as identifying outliers which in themselves could be very interesting for the data analyst. The ideas explored in this paper are similar to those that have been employed in generation of multiple classifier systems (classifier ensembles) [7][8][9][10][11][12][13] where the so called unstable classifiers (i.e. classifiers like decision trees or some neuro-fuzzy classifiers, the performance of which can be significantly affected by the presence of outliers) have been stabilized through the use of classifier ensembles.…”

Section: Introductionmentioning

confidence: 99%

Outlier Resistant PCA Ensembles

Gabrys

Baruque

Corchado

2006

Lecture Notes in Computer Science

Self Cite

View full text Add to dashboard Cite

Abstract. Statistical re-sampling techniques have been used extensively and successfully in the machine learning approaches for generation of classifier and predictor ensembles. It has been frequently shown that combining so called unstable predictors has a stabilizing effect on and improves the performance of the prediction system generated in this way. In this paper we use the resampling techniques in the context of Principal Component Analysis (PCA). We show that the proposed PCA ensembles exhibit a much more robust behaviour in the presence of outliers which can seriously affect the performance of an individual PCA algorithm. The performance and characteristics of the proposed approaches are illustrated on a number of experimental studies where an individual PCA is compared to the introduced PCA ensemble.

show abstract

“…Zhou et al have analyzed the effect on the number of participating classifiers into ensemble in both theoretical and empirical studies [7]. Bagging and boosting have been actively investigated to generate the base classifiers as popular ensemble learning techniques, while various fusion strategies have also been studied for effective ensemble [3,4,8]. A survey on generating diverse classifiers for ensemble has been conducted by Brown [9].…”

Section: Introductionmentioning

confidence: 99%

“…Since ensembling the same classifiers does not produce any elevation on performance [8], selecting diverse as well as accurate base classifiers is very important in making a good ensemble classifier [9]. Simple ways to generate various classifiers are randomly initializing parameters or making a variation of training data.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

Hong

Cho

2005

Modeling Decisions for Artificial Intelligence

View full text Add to dashboard Cite

Abstract. Combining a set of classifiers has often been exploited to improve the classification performance. Accurate as well as diverse base classifiers are prerequisite to construct a good ensemble classifier. Therefore, estimating diversity among classifiers has been widely investigated. This paper presents an ensemble approach that combines a set of diverse rules obtained by genetic programming. Genetic programming generates interpretable classification rules, and diversity among them is directly estimated. Finally, several diverse rules are combined by a fusion method to generate a final decision. The proposed method has been applied to cancer classification using gene expression profiles, which is one of the important issues in bioinformatics. Experiments on several popular cancer datasets have demonstrated the usability of the method. High performance of the proposed method has been obtained, and the accuracy has increased by diversity among the base classification rules.

show abstract

Classifier selection for majority voting

Cited by 542 publications

References 32 publications

A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

A Study on the Use of Multiobjective Genetic Algorithms for Classifier Selection in FURIA-based Fuzzy Multiclassifiers

Outlier Resistant PCA Ensembles

Cancer Prediction Using Diversity-Based Ensemble Genetic Programming

Contact Info

Product

Resources

About