Learning from labeled and unlabeled data

Due to the considerable time and expense required in labeling data, a challenge is to propose learning algorithms that can learn from a small amount of labeled data and a much larger amount of unlabeled data. In this paper, we propose one such algorithm which uses an evolutionary strategy to iteratively adjust the class membership of the patterns in the unlabeled sample. The iterative adjustment is done so that the class conditional distribution obtained from such a labeling allows a maximum a posteriori classification with minimum classification error on the labeled patterns. We detail the algorithm and provide results obtained by the proposed algorithm on 5 different datasets.