Information and Rough Set Theory Based Feature Selection Techniques
Feature selection is a well known and studied technique that aims to solve “the curse of dimensionality” and improve performance by removing irrelevant and redundant features. This paper highlights some well known approaches to filter feature selection, information theory and rough set theory, and compares a recent fitness function with some traditional methods. The contributions of this paper are two-fold. First, new results confirm previous research and show that the recent fitness function can also perform favorably when compared to rough set theory. Secondly, the measure of redundancy that is used in traditional information theory is shown to damage the performance when a similar approach is applied to the recent fitness function.
KeywordsFeature Selection Mutual Information Feature Subset Feature Selection Algorithm Joint Entropy
Unable to display preview. Download preview PDF.
- 5.Cervante, L., Bing, X., Zhang, M.: Binary particle swarm optimisation for feature selection: A filter based approach. In: Proceedings of 2012 IEEE Congress on Evolutionary Computation, pp. 881–888. IEEE Press (2012)Google Scholar
- 6.Shannon, C.E., Weaver, W.: A Mathematical Theory of Communication. University of Illinois Press, Champaign (1963)Google Scholar
- 7.Pawlak, Z.: Rough sets. International Journal of Parallel Programming 11, 341–356 (1982), 10.1007/BF01001956Google Scholar