Combining supervised classifiers with unlabeled data

来源期刊:中南大学学报(英文版)2016年第5期

论文作者:张雪英 刘雪艳 李凤莲 黄丽霞

文章页码:1176 - 1182

Key words:correntropy; unlabeled data; regularization framework; ensemble learning

Abstract: Ensemble learning is a wildly concerned issue. Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers. They fail to address the ensemble task where only unlabeled data are available. A label propagation based ensemble (LPBE) approach is proposed to further combine base classification results with unlabeled data. First, a graph is constructed by taking unlabeled data as vertexes, and the weights in the graph are calculated by correntropy function. Average prediction results are gained from base classifiers, and then propagated under a regularization framework and adaptively enhanced over the graph. The proposed approach is further enriched when small labeled data are available. The proposed algorithms are evaluated on several UCI benchmark data sets. Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.

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