Exploiting Unlabeled Data to Enhance Ensemble Diversity

Ensemble learning aims to improve generalization ability by using multiple base learners. It is well-known that to construct a good ensemble, the base learners should be accurate as well as diverse. In this paper, unlabeled data is exploited to facilitate ensemble learning by helping augment the diversity among the base learners. Specifically, a semisupervised ensemble method named UDEED is proposed. Unlike existing semi-supervised ensemble methods where error-prone pseudo-labels are estimated for unlabeled data to enlarge the labeled data to improve accuracy, UDEED works by maximizing accuracies of base learners on labeled data while maximizing diversity among them on unlabeled data. Experiments show that UDEED can effectively utilize unlabeled data for ensemble learning and is highly competitive to well-established semisupervised ensemble methods. Keywords-ensemble learning; unlabeled data; diversity

• Type: Journal
• Year: 2010
• Where: ICDM
• Authors: Min-Ling Zhang, Zhi-Hua Zhou
• Keywords: Base Learners, Data Mining, ICDM 2010, Semisupervised Ensemble Methods, Unlabeled Data