Semi-supervised robust alternating AdaBoost

Semi-Supervised Learning is one of the most popular and emerging issues in Machine Learning. Since it is very costly to label large amounts of data, it is useful to use data sets without labels. For doing that, normally Semi-Supervised Learning is used to improve the performance or efficiency of classification algorithms. This paper intends to use the techniques of Semi-Supervised Learning to boost the performance of the Robust Alternating AdaBoost algorithm. We introduce the algorithm RADA+ and compare it with RADA, reporting the performance results using synthetic and real data sets, the latter obtained from a benchmark site.