Feature Selection Ensemble for Symbolic Data Classification with AHP

Meiqian Wang, Xiaodong Yue, Can Gao, Yufei Chen

Abstract: The ensemble of feature selections facilitates to improve data generalization for learning tasks. However, existing feature selection ensemble methods have the following drawbacks. First, focusing on the numerical data, the works on the feature selection ensemble for symbolic and mixed-type data are very limited. Second, the voting-based ensemble strategies tend to select the top significant features but the consistency between the ensemble result and the diverse feature selections cannot be guaranteed. Aiming to handle these problems, we propose an feature selection ensemble method based on Analytic Hierarchy Process (AHP) in this paper. The AHP-based ensemble method can integrate diverse feature selections into a consistent one under the multiple criteria of feature discernibility and independence. Moreover, the ensemble methodology is helpful to implement the feature selection on distributed data and involve domain knowledge through extending criteria. Experimental results validate that the proposed ensemble method of feature selections is effective for symbolic data classification.

External IDs:dblp:conf/icpr/WangYGC18