Rough set based features ensemble learning

The minimal reduct of features in the Rough Set theory is a criterion to select the best feature subset based on it´s ability to discriminate objects. In this paper, a multi-class features ensemble learning algorithm based on feature reduct is presented. The algorithm maintains a weight distribution in train set, which is used to compute minimal approximate reduct of features in each iteration of algorithm. Weak classifiers are constructed from the minimal approximate reduct and the weight distribution is updated according to examples in the train set which have been misclassified. The ensemble classifier are constructed by weighted votes of all weak classifiers. The results of testing in several dataset show that the algorithm has high accuracy of prediction and strong ability of generalization.