Title :
Feature Selection with Integrated Relevance and Redundancy Optimization
Author :
Linli Xu;Qi Zhou;Aiqing Huang;Wenjun Ouyang;Enhong Chen
Author_Institution :
Sch. of Comput. Sci. &
Abstract :
The task of feature selection is to select a subset of the original features according to certain predefined criterion with the goal to remove irrelevant and redundant features, improve the prediction performance and reduce the computational costs of data mining algorithms. In this paper, we integrate feature relevance and redundancy explicitly in the feature selection criterion. Spectral feature analysis is applied here which can fit into both supervised and unsupervised learning problems. Specifically, we formulate the problem into a combinatorial problem to maximize the relevance and minimize the redundancy of the selected subset of features at the same time. The problem can be relaxed and solved with an efficient extended power method with global convergence guaranteed. Extensive experiments demonstrate the advantages of the proposed technique in terms of improving the prediction performance and reducing redundancy in data.
Keywords :
"Redundancy","Optimization","Laplace equations","Data mining","Convergence","Correlation","Prediction algorithms"
Conference_Titel :
Data Mining (ICDM), 2015 IEEE International Conference on
DOI :
10.1109/ICDM.2015.121