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Erschienen in:
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2019 | OriginalPaper | Buchkapitel

Performance Evaluation and Analysis of Feature Selection Algorithms

verfasst von : Tanuja Pattanshetti, Vahida Attar

Erschienen in: Data Management, Analytics and Innovation

Verlag: Springer Singapore

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Abstract

Exorbitant data of huge dimensionality is generated because of wide application of technologies nowadays. Intent of using this data for decision-making is greatly affected because of the curse of dimensionality as selection of all features will lead to over-fitting and ignoring the relevant ones can lead to information loss. Feature selection algorithms help to overcome this problem by identifying the subset of original features by retaining relevant features and by removing the redundant ones. This paper aims to evaluate and analyze some of the most popular feature selection algorithms using different benchmarked datasets K-means Clustering, Relief, Relief-F, Random Forest (RF) algorithms are evaluated and analyzed in the form of combinations of different rankers and classifiers. It is observed empirically that the accuracy of the ranker and classifier varies from dataset to dataset. Novel concept of applying Multivariate co-relation analysis (MCA) for feature selection is made and results show improved performance over legacy based feature selection algorithms.

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Metadaten
Titel
Performance Evaluation and Analysis of Feature Selection Algorithms
verfasst von
Tanuja Pattanshetti
Vahida Attar
Copyright-Jahr
2019
Verlag
Springer Singapore
Buch
Data Management, Analytics and Innovation

Print ISBN: 978-981-13-1401-8

Electronic ISBN: 978-981-13-1402-5

Copyright-Jahr: 2019

DOI
https://doi.org/10.1007/978-981-13-1402-5_4

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