Prototype selection for dynamic classifier and ensemble selection

Cruz, Rafael M. O.; Sabourin, Robert; Cavalcanti, George D. C.

doi:10.1007/s00521-016-2458-6

Cited by 36 publications

(20 citation statements)

References 21 publications

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“…The noise sensitivity drawback is important because DES techniques are highly sensitive to noise, outliers, and high level of overlap between classes in D SEL [2,15]. Figure 2 The Overall Local Accuracy (OLA) [7] DES technique estimates the competence of classifiers using their accuracy in the region of competence, that is, the more samples a classifier correctly classifies, the more competent it is.…”

Section: Drawback 1: Noise Sensitivitymentioning

confidence: 99%

“…Moreover, the uses of KNNE complements the filtering stage of the FIRE-DES++ framework. By reducing the overlap between the classes, the filtering phase may remove important samples that are close to the class borders [16,2], which could make indecision regions being mistaken as safe regions. By using the KNNE, the FIRE-DES++ framework guarantees that the DFP mechanism will be employed in such scenarios.…”

Section: Region Of Competence Definition Phasementioning

confidence: 99%

“…Given a test sample and a pool of classifiers, DES techniques select one or more competent classifiers for the classification of that test sample. The most important part in DES techniques is how to evaluate the competence level of each base classifier for the classification of a given test sample [2]. In general, DES techniques evaluate the competence level of base classifiers for the classification of a test sample, x query , based on the performance of the base classifier in a local region surrounding the test sample, named region of competence.…”

Section: Introductionmentioning

confidence: 99%

“…Hence, the FIRE framework will not mistake a noisy region for an indecision region when estimating the regions of competence. (2) During the test phase, the FIRE-DES++ employs a K-Nearest Neighbors Equality (KNNE) [5] to define the region of competence. The KNNE is a variation of the KNN technique which selects the same amount of samples from each class.…”

Section: Introductionmentioning

confidence: 99%

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FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

Cruz

Oliveira

Cavalcanti

et al. 2019

Pattern Recognition

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Dynamic Ensemble Selection (DES) techniques aim to select one or more competent classifiers for the classification of each new test sample. Most DES techniques estimate the competence of classifiers using a given criterion over the region of competence of the test sample, usually defined as the set of nearest neighbors of the test sample in the validation set. Despite being very effective in several classification tasks, DES techniques can select classifiers that classify all samples in * Corresponding author datasets. Experimental results show that FIRE-DES++ increases the classification performance of all DES techniques considered in this work, outperforming FIRE-DES with 7 out of the 8 DES techniques, and outperforming state-of-the-art DES frameworks.

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Section: Drawback 1: Noise Sensitivitymentioning

confidence: 99%

Section: Region Of Competence Definition Phasementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

Cruz

Oliveira

Cavalcanti

et al. 2019

Pattern Recognition

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“…In a nutshell, the following research questions are investigated in this paper: 1) Is the ENN [12] the best PS technique for dynamic selection techniques? 2) Do different prototype selection techniques improve the classification performance of dynamic selection techniques?…”

Section: Introductionmentioning

confidence: 99%

Analyzing different prototype selection techniques for dynamic classifier and ensemble selection

Cruz

Sabourin

Cavalcanti

2017

2017 International Joint Conference on Neural Networks (IJCNN)

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In dynamic selection (DS) techniques, only the most competent classifiers, for the classification of a specific test sample are selected to predict the sample's class labels. The more important step in DES techniques is estimating the competence of the base classifiers for the classification of each specific test sample. The classifiers' competence is usually estimated using the neighborhood of the test sample defined on the validation samples, called the region of competence. Thus, the performance of DS techniques is sensitive to the distribution of the validation set. In this paper, we evaluate six prototype selection techniques that work by editing the validation data in order to remove noise and redundant instances. Experiments conducted using several state-of-the-art DS techniques over 30 classification problems demonstrate that by using prototype selection techniques we can improve the classification accuracy of DS techniques and also significantly reduce the computational cost involved.

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A Dynamic Ensemble Selection Framework Using Dynamic Weighting Approach

Qadeer¹,

Qamar²

2019

Advances in Intelligent Systems and Computing

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Prototype selection for dynamic classifier and ensemble selection

Cited by 36 publications

References 21 publications

FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

FIRE-DES++: Enhanced online pruning of base classifiers for dynamic ensemble selection

Analyzing different prototype selection techniques for dynamic classifier and ensemble selection

A Dynamic Ensemble Selection Framework Using Dynamic Weighting Approach

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