A combination classification method based on Ripper and Adaboost

Abstract

With the growing demand for data analysis, machine learning technology has been widely used in many applications, such as mass data summarising rules, predicting behaviours and dividing characteristics. The Ripper algorithm presents better pruning and stopping criteria than the traditional decision tree algorithm (C4.5), while its error rate less than or equal to C4.5 by O(nlog2n) time complexity. As a result of that, Ripper can maintain high efficiency even on the massive dataset which contains lots of noise. Adaboost is one of iterative algorithms, which combines a group of weak classifiers together to set up a strong classifier. In order to improve the accuracy of Ripper classification algorithm and reduce the computational complexity, this paper proposes a Ripper-Adaboost combined classification method (Ripper-ADB). The experiment result shows Ripper-ADB could improve the classifier and get higher classification accuracy than decision tree and SVM.