Feature selection based on information theory, consistency and separability indices

Duch, Włodzisław; Grąbczewski, Krzysztof; Winiarski, Tomasz; Biesiada, Jacek; Kachel, A. Friederike

doi:10.1109/iconip.2002.1199014

Cited by 9 publications

(2 citation statements)

References 7 publications

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“…Recent work in this category includes LFDA [37], sparse LDA [33] and CDA [30]. Information-theoretic measures, typically aimed at evaluating the entropy or the mutual information of features, have been adopted in various feature ranking or feature subset assessment methods (see, for instance, [2], [27], [12], [35] and [7]). Dependence/correlation measures quantify the ability to predict the value of one variable from the value of another variable.…”

Section: Ranking/selecting Featuresmentioning

confidence: 99%

A direct measure of discriminant and characteristic capability for classifier building and assessment

Armano

2015

Information Sciences

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Performance measures are used in various stages of the process aimed at solving a classifi-\ud cation problem. Unfortunately, most of these measures are in fact biased, meaning that they\ud strictly depend on the class ratio – i.e. on the imbalance between negative and positive samples.\ud After pointing to the source of bias for the best known measures, novel unbiased measures\ud are defined which are able to capture the concepts of discriminant and characteristic capability.\ud The combined use of these measures can give important information to researchers\ud involved in machine learning or pattern recognition tasks, in particular for classifier performance\ud assessment and feature selection

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Section: Ranking/selecting Featuresmentioning

confidence: 99%

A direct measure of discriminant and characteristic capability for classifier building and assessment

Armano

2015

Information Sciences

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“…Ranking of features determines the importance of any individual feature, neglecting their possible interactions. Ranking methods are based on statistics, information theory or on some functions of the classifier's outputs, as studied by Duch et al (10) .…”

Section: Related Workmentioning

confidence: 99%

Large-scale data analysis on aviation accident database using different data mining techniques

Christopher¹,

Vivekanandam²,

Anderson

et al. 2016

Aeronaut. j.

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Data mining is an iterative process in which progress is defined by discovery through either automatic or manual methods. A data cleaning procedure is proposed to improve the quality of classification tasks in the knowledge discovery process by taking into account both redundant and conflicting data. The redundancy check is performed on the original dataset and the resultant dataset is preserved. This resultant dataset is then checked for conflicting data and, if any are found, they are corrected and updated on the original aircraft dataset. This updated dataset is then classified using a variety of classifiers such as Bayes, functions, lazy, MISC, rules and decision trees. The performance of the updated datasets on these classifiers is examine, and the result shows a significant improvement in the classification accuracy after redundancy and conflicts are removed. The conflicts after correction are updated in the original dataset, and when the performance of the classifier is evaluated, great improvement is observed. This paper aims to address how data mining techniques can be used to understand complex system accidents in the aviation domain. Decision trees are considered to be the one of the most powerful and popular approaches in knowledge discovery and data mining. The objective is to develop a classification model for aviation risk investigation and reduction using a decision tree induction method that enhances the ability to form decision trees and thereby proves that the classification accuracy of decision trees is greater. Different feature selectors are used in this study in order to reduce the number of initial attributes.

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Data Balancing for a More Accurate Model of Bacterial Vaginosis Diagnosis

Pérez-Gómez

Canul-Reich

Rivera-López

et al. 2023

Algorithms for Intelligent Systems

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Feature selection based on information theory, consistency and separability indices

Cited by 9 publications

References 7 publications

A direct measure of discriminant and characteristic capability for classifier building and assessment

A direct measure of discriminant and characteristic capability for classifier building and assessment

Large-scale data analysis on aviation accident database using different data mining techniques

Data Balancing for a More Accurate Model of Bacterial Vaginosis Diagnosis

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