Feature Subset Selection by Particle Swarm Optimization with Fuzzy Fitness Function
Feature extraction or Feature subset selection is an important preprocessing task for pattern recognition,data mining or machine learning application.Feature subset selection basically depends on selecting a criterion function for evaluation of the feature subset and a search strategy to find the best feature subset from a large number of feature subsets.Lots of techniques have been developed so far,mainly from statistical theory,still research is going on to find better solutions in terms of optimality and computational ease.Recently soft computing techniques are gaining popularity for solving real world problems for their more flexibility compared to statistical or mathematical techniques.In this work an algorithm based on particle swarm optimization with fuzzy fitness function has been proposed for getting optimal feature subset from a feature set with large number of features. Simple simulation experiments with two benchmark data sets show that the proposed method is similar in performance to the results reported earlier and is computationaily less demanding in comparison to Genetic AIogrithm,another population based evolutionary search technique proposed eariler for feature subset selection by author.
Basabi Chakraborty
Faculty of Software and Information Science Iwate Prefectural University Japan,020-0193
国际会议
厦门
英文
1038-1042
2008-11-17（万方平台首次上网日期，不代表论文的发表时间）