Abstract
In this paper, a novel feature selection algorithm FEAST is proposed based on association rule mining. The proposed algorithm first mines association rules from a data set; then, it identifies the relevant and interactive feature values with the constraint association rules whose consequent is the target concept, detects and eliminates the redundant feature values with the constraint association rules whose consequent and antecedent are both of single feature value. Finally, it obtains the feature subset by mapping the feature values to the corresponding features. As the support and confidence thresholds are two important parameters in association rule mining and play a vital role in FEAST, a partial least square regression (PLSR) based threshold prediction method is presented as well. The effectiveness of FEAST is tested on both synthetic and real world data sets, and the classification results of five different types of classifiers with seven representative feature selection algorithms are compared. The results on the synthetic data sets show that FEAST can effectively identify irrelevant and redundant features while reserving interactive ones. The results on the real world data sets show that FEAST outperforms other feature selection algorithms in terms of classification accuracies. In addition, the PLSR based threshold prediction method is performed on the real world data sets, and the results show it works well in recommending proper support and confidence thresholds for FEAST.