Feature Interaction Maximisation

• A new feature selection method called Feature Interaction Maximisation is proposed.
• It employs interaction information with maximum of the minimum criterion.
• It uses forward greedy search to select the features one by one.
• It is evaluated in terms of the classification accuracy of the selected features.
• The result shows that the proposed method outperforms IG, mRMR, DISR, and IGFS.

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.