Feature selection for multi-label data by using simulated annealing
Jiexin Chen · Jisuanji gongcheng yu sheji · 2011
There are many redundant features in the data sets,in order to remove the irrelevant and redundant features in the multi-label data and further to improve the generalization performance of multi-label learning algorithms,simulated annealing based feature selection for multi-label data(SAML) is proposed,which employs the simulated annealing algorithm to search the optimal subsets.We know simulated annealing algorithm perform better than genetic algorithm.Experiments on Yahoo web page categorization data sets,which are widely used for benchmark evaluation,show that the performance of SAML is superior to some state-of-arts multi-label dimensionality reduction methods.