关键词: 组合分类器, 特征提取, 主成分分析

Abstract: A new method, called PCARules, is presented for constructing ensembles of rule-based classifiers. Although the class label of a sample to be classified is also determined by taking weighted vote among the predictions made by each base classifier, this method is very different from bagging and boosting in the way of creating the training data for a base classifier. Instead of creating a training data for each base classifier by sampling, this method splits the feature set into K subsets randomly, upon each of which principal component analysis (PCA) is applied to find the corresponding principal components. And then all principal components are put together to form a new feature space, into which all original training data are mapped to create the training set for a base classifier. Experiments carried on 30 benchmark datasets selected randomly from the UCI machine learning repository show that the method not only improves performance of rule-based classifiers significantly, but also achieves higher accuracy in most of data sets than traditional combining methods such as bagging and boosting.

Key words: classifier ensemble, feature extraction, principal component analysis (PCA)