A feature subset selection algorithm based on equal interval division and three-way interaction information

Some existing feature subset selection algorithms use only one metric, such as symmetric uncertainty, to evaluate redundant features. These algorithms have the problem that some relevant features are considered as redundant and removed. To solve the problem, this paper proposes a feature subset selection algorithm based on equal interval division and three-way interaction information. Symmetric uncertainty between each feature and the class label is first calculated and compared with zero, and irrelevant features are removed. Then, symmetric uncertainty between features is calculated and compared with symmetric uncertainty between features and the class label. The method of equal interval division and ranking is adopted to process symmetric uncertainty between features and the class label as well as symmetric uncertainty between features, and these processed parts are then compared. Three-way interaction information among features and the class label is calculated and compared with zero. The three results of the comparisons are employed to remove redundant features. To validate the performance, the proposed algorithm is compared with several feature subset selection algorithms. Experimental results demonstrate that the proposed algorithm can achieve better feature selection performance.

Automated summary

The paper proposes a new feature subset selection algorithm based on equal interval division and three-way interaction information to address the issues existing in some current algorithms. Experimental results demonstrate that the proposed algorithm can achieve better feature selection performance.