Nowadays, overfitting is becoming a more and more important issue in the aspect of machine learning, especially in attribute reduction, the problem of overfitting has caused a lot of trouble. In order to solve this problem, this paper proposed an algorithm that integrated ensemble strategy and removal operation. First of all, this algorithm divided the data sets into M groups in the same size. Then, it adopted the ensemble strategy to potential attribute reduction for the M-1 groups. Finally, it tested the remaining 1 group in advance. Once overfitting had occurred, it removed the newly joined properties from the potential attribute reduction. It used this method to test potential attributes in advance to prevent overfitting in the actual application. The experimental results of several UCI datasets show the validity of the new algorithm, which may provide a new direction for enriching and developing attribute reduction.

[2020-09-05] Printed Article /doc/2019.05.0116/v2