Conference Proceedings

Feature selection for multiclass binary data

K Perera, J Chan, S Karunasekera

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Springer Nature | Published : 2018

Abstract

© Springer International Publishing AG, part of Springer Nature 2018. Feature selection in binary datasets is an important task in many real world machine learning applications such as document classification, genomic data analysis, and image recognition. Despite many algorithms available, selecting features that distinguish all classes from one another in a multiclass binary dataset remains a challenge. Furthermore, many existing feature selection methods incur unnecessary computation costs for binary data, as they are not specifically designed for binary data. We show that exploiting the symmetry and feature value imbalance of binary datasets, more efficient feature selection measures that..

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