2021/04/10 by Qiuqiang Lin, Lin, Qiuqiang, Chuanhou Gao +1 · 1 citation
Computer Science · #Data Mining Algorithms and Applications #Databases (cs.DB) #FOS: Computer and information sciences #Machine Learning (cs.LG) #cs.DB #cs.LG
paper · pdf · doi:10.48550/arxiv.2104.04728
arxiv created 2021/04/10 · openalex publication_date 2021/04/10 · arxiv updated 2021/04/13 · openalex created_date 2021/05/10 · openalex updated_date 2026/07/28
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.