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Feature Interaction Maximisation
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AI
DOI：10.1016/j.patrec.2013.04.002.png)
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Abstract
En 中文
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, Naive 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. (C) 2013 Elsevier B.V. All rights reserved.
Keywords:
Feature selection
Interaction information
Mutual information
Subset feature selection
Classification
Dimensionality reduction