  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.
