Rotation-based ensemble classifiers for high-dimensional data

Abstract

In past 20 years, Multiple Classifier System (MCS) has shown great potential to improve the accuracy and reliability of pattern classification. In this chapter, we discuss the major issues of MCS, including MCS topology, classifier generation, and classifier combination, providing a summary of MCS applied to remote sensing image classification, especially in high-dimensional data. Furthermore, the recently rotation-based ensemble classifiers, which encourage both individual accuracy and diversity within the ensemble simultaneously, are presented to classify high-dimensional data, taking hyperspectral and multidate remote sensing images as examples. Rotation-based ensemble classifiers project the original data into a new feature space using feature extraction and subset selection methods to generate the diverse individual classifiers. Two classifiers: Decision Tree (DT) and Support Vector Machine (SVM), are selected as the base classifier. Unsupervised and supervised feature extraction methods are employed in the rotation-based ensemble classifiers. Experimental results demonstrated that rotation-based ensemble classifiers are superior to Bagging, AdaBoost and random-based ensemble classifiers.