Random subspace method based on Canonical Correlation Analysis

Random subspace method (RSM) is a successful ensemble construction technique for classification and its success mainly lies in that it could generate quite diverse component classifiers. However, the recognition accuracy of the component classifier is often insufficient due to its random selection of inputs. In this paper, to improve the accuracy of the component classifier and further gain high performance ensemble classifier, I introduce the idea of information fusion into RSM and propose a new method called RS CCA. RS CCA fuses randomly selected features and global features using Canonical Correlation Analysis (CCA) method, so it can obtain the feature sets containing global information. The experiments on 13 UCI datasets show RS CCA is very effective to improve the performance of RSM. In addition, an analysis about average diversity and average accuracy is given to explain why RS CCA can yield better performance than RSM.