An AdaBoost Algorithm for Multiclass Semi-supervised Learning

Jafar Tanha, Maarten van Someren, Hamideh Afsarmanesh
2012 2012 IEEE 12th International Conference on Data Mining  
An AdaBoost algorithm for multiclass semi-supervised learning Tanha, J.; van Someren, M.W.; Afsarmanesh, H. Abstract-We present an algorithm for multiclass Semi-Supervised learning which is learning from a limited amount of labeled data and plenty of unlabeled data. Existing semisupervised algorithms use approaches such as one-versus-all to convert the multiclass problem to several binary classification problems which is not optimal. We propose a multiclass semisupervised boosting algorithm
more » ... sting algorithm that solves multiclass classification problems directly. The algorithm is based on a novel multiclass loss function consisting of the margin cost on labeled data and two regularization terms on labeled and unlabeled data. Experimental results on a number of UCI datasets show that the proposed algorithm performs better than the stateof-the-art boosting algorithms for multiclass semi-supervised learning.
doi:10.1109/icdm.2012.119 dblp:conf/icdm/TanhaSA12 fatcat:hrwoirfupngq3jzaagohauvisy