In this paper a new scheme of feature ranking and hence feature selection using a Multilayer Perception (MLP) Network has been proposed. The novelty of the proposed MLP-based scheme and its difference from another MLP-based feature ranking scheme have been analyzed. In addition we have modified an existing feature ranking/selection scheme based on fuzzy entropy. Empirical investigations show that the proposed MLP-based scheme is superior to the other schemes implemented
This thesis introduces two novel machine learning methods of feature ranking and feature selection....
Features gathered from the observation of a phenomenon are not all equally informative: some of them...
AbstractBefore a pattern classifier can be properly designed, it is necessary to consider the featur...
The article provides a fuzzy set theoretic feature evaluation index and a connectionist model for it...
A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature ...
The present article demonstrates a way of formulating a neuro-fuzzy approach for feature extraction ...
Recent developments in technology have led to accelerated growth of data, and the associated challen...
Demonstrates a way of formulating neuro-fuzzy approaches for both feature selection and extraction u...
AbstractFeature selection has become interest to many research areas which deal with machine learnin...
Features gathered from the observation of a phenomenon are not all equally informative: some of them...
Abstract: We presented a comparison between several feature ranking methods used on two real dataset...
International audienceFeature selection becomes the focus of much research in many areas of applicat...
A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stoppin...
This paper presents a methodological approach for developing image classifiers that work by exploiti...
Abstract—Dataset dimensionality is undoubtedly the single most significant obstacle which exasperate...
This thesis introduces two novel machine learning methods of feature ranking and feature selection....
Features gathered from the observation of a phenomenon are not all equally informative: some of them...
AbstractBefore a pattern classifier can be properly designed, it is necessary to consider the featur...
The article provides a fuzzy set theoretic feature evaluation index and a connectionist model for it...
A neuro-fuzzy methodology is described which involves connectionist minimization of a fuzzy feature ...
The present article demonstrates a way of formulating a neuro-fuzzy approach for feature extraction ...
Recent developments in technology have led to accelerated growth of data, and the associated challen...
Demonstrates a way of formulating neuro-fuzzy approaches for both feature selection and extraction u...
AbstractFeature selection has become interest to many research areas which deal with machine learnin...
Features gathered from the observation of a phenomenon are not all equally informative: some of them...
Abstract: We presented a comparison between several feature ranking methods used on two real dataset...
International audienceFeature selection becomes the focus of much research in many areas of applicat...
A feature ranking scheme for multilayer perceptron (MLP) ensembles is proposed, along with a stoppin...
This paper presents a methodological approach for developing image classifiers that work by exploiti...
Abstract—Dataset dimensionality is undoubtedly the single most significant obstacle which exasperate...
This thesis introduces two novel machine learning methods of feature ranking and feature selection....
Features gathered from the observation of a phenomenon are not all equally informative: some of them...
AbstractBefore a pattern classifier can be properly designed, it is necessary to consider the featur...