Semi-supervised multi-class Adaboost by exploiting unlabeled data

• DOI: 10.1016/j.eswa.2010.11.062
• Publication Date: 2010-11-18T04:30:33Z
• Authors: Enmin Song; Dongshan Huang; Guangzhi Ma; Chih-Cheng Hung
• Keywords: Boosting; AdaBoost; One-class classification; Supervised Learning; Binary classification; Multiclass classification; Benchmark (surveying)

Research highlights? We propose a semi-supervised learning method by using the multi-class boosting. ? It handles K-class classification without reducing into multiple two-class problems. ? The classification accuracy of base classifier requires only 1/K or better. ? Higher classification accuracy is achieved by exploiting the unlabeled data. Semi-supervised learning has attracted much attention in pattern recognition and machine learning. Most semi-supervised learning algorithms are proposed for binary classification, and then extended to multi-class cases by using approaches such as one-against-the-rest. In this work, we propose a semi-supervised learning method by using the multi-class boosting, which can directly classify the multi-class data and achieve high classification accuracy by exploiting the unlabeled data. There are two distinct features in our proposed semi-supervised learning approach: (1) handling multi-class cases directly without reducing them to multiple two-class problems, and (2) the classification accuracy of each base classifier requiring only at least 1/K or better than 1/K (K is the number of classes). Experimental results show that the proposed method is effective based on the testing of 21 UCI benchmark data sets.