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Feature interaction maximisation

Bennasar, Mohamed, Setchi, Rossitza and Hicks, Yulia Alexandrovna 2013. Feature interaction maximisation. Pattern Recognition Letters 34 (14) , pp. 1630-1635. 10.1016/j.patrec.2013.04.002

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Abstract

Feature selection plays an important role in classification algorithms. It is particularly useful in dimensionality reduction for selecting features with high discriminative power. This paper introduces a new feature-selection method called Feature Interaction Maximisation (FIM), which employs three-way interaction information as a measure of feature redundancy. It uses a forward greedy search to select features which have maximum interaction information with the features already selected, and which provide maximum relevance. The experiments conducted to verify the performance of the proposed method use three datasets from the UCI repository. The method is compared with four other well-known feature-selection methods: Information Gain (IG), Minimum Redundancy Maximum Relevance (mRMR), Double Input Symmetrical Relevance (DISR), and Interaction Gain Based Feature Selection (IGFS). The average classification accuracy of two classifiers, Naïve Bayes and K-nearest neighbour, is used to assess the performance of the new feature-selection method. The results show that FIM outperforms the other methods.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Uncontrolled Keywords: Feature selection; Interaction information; Mutual information; Subset feature selection; Classification; Dimensionality reduction
Publisher: Elsevier
ISSN: 0167-8655
Last Modified: 04 Jun 2017 04:58
URI: http://orca.cf.ac.uk/id/eprint/46887

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