A Classifier Ensemble Algorithm Based on Local Random Subspace

Classifier ensemble learning is one of the present research focuses in machine learning field.However,the classical method of completely random subspace selecting can not guarantee good performances of sub-classifiers for high dimension datasets.Therefore,a classifier ensemble algorithm based on local random subspace is proposed.The features are ranked by employing feature selection strategy firstly,and then the ranked feature list is partitioned into a few parts and the randomly feature is selected in each part according to the given sampling rate.Thus,the performances of sub-classifiers and their diversities are improved.Experiments are carried out on 5 UCI datasets and 5 gene datasets.The experimental results show that the proposed algorithm is superior to a single classifier,and in most cases it is better than those classical classifier ensemble methods.