Attributes Selection Based on Correlation Analysis and Genetic Algorithm
Fenggang Li · Jisuanji gongcheng · 2010
It is an important task for knowledge-based systems to select and evaluate the attributes as well as a critical factor affecting systems' performance.Using the genetic operator of the searching approach and correlation analysis,which characterizes Genetic Algorithm(GA),as the evaluation mechanism,this paper presents a new method to select the optimal subset of attributes for a given case library.Experimental results show that the proposed method can identify the most related subset to classify and predict,while reducing the representation space of the attributes whereas hardly decreasing the classification precision.