Improving BAS committee performance with a semi-supervised approach

Semi-supervised Learning is a machine learning approach that, by making use of both labeled and unlabeled data for training, can significantly improve learning accuracy. Boosting is a machine learning technique that combines several weak classifiers to improve the overall accuracy. At each iteration, the algorithm changes the weights of the examples and builds an additional classifier. A well known algorithm based on boosting is AdaBoost, which uses an initial uniform distribution. Boosting At Start (BAS) is a boosting framework that generalizes AdaBoost by allowing any initial weight distribution and a cost function. Here, we present a scheme that allows the use of unlabeled data in the BAS framework. We examine the performance of the proposed scheme in some datasets commonly used in semi-supervised approaches. Our empirical findings indicate that BAS can improve the accuracy of the generated classifiers by taking advantage of unlabeled data.