A dynamic feature selection method based on combination of GA with K-means
Wei Zhao, Yafei Wang, Dan Li · 2010
In view of the high-dimensional feature in text categorization influence the accuracy and efficiency of classification. The paper presents a dynamic feature selection method based on combination of k-means algorithm with genetic algorithm, called K-GA,which uses the genetic algorithm (GA) optimization features to implement global searching,and uses k-means algorithm to selection operation to control the scope of the search, ensure the validity of each gene and the speed of convergence. Ultimate select a feature subset which has strong distinguish ability. The experimental results show that the method can effectively reduce the feature dimension, to improve text classification accuracy and efficiency.