Discovering Categorical Main and Interaction Effects Based on Association Rule Mining

TL;DR

This paper introduces a novel method that leverages association rule mining to select relevant features and their interactions in high-dimensional categorical data, improving feature selection efficiency and effectiveness.

Contribution

It proposes a new algorithm that uses association rules for feature and interaction selection, addressing high dimensionality issues in categorical data.

Findings

Algorithm is computationally efficient.

Experimental results verify effectiveness.

Improves feature selection in high-dimensional data.

Abstract

With the growing size of data sets, feature selection becomes increasingly important. Taking interactions of original features into consideration will lead to extremely high dimension, especially when the features are categorical and one-hot encoding is applied. This makes it more worthwhile mining useful features as well as their interactions. Association rule mining aims to extract interesting correlations between items, but it is difficult to use rules as a qualified classifier themselves. Drawing inspiration from association rule mining, we come up with a method that uses association rules to select features and their interactions, then modify the algorithm for several practical concerns. We analyze the computational complexity of the proposed algorithm to show its efficiency. And the results of a series of experiments verify the effectiveness of the algorithm.