期刊
JOURNAL OF INTELLIGENT INFORMATION SYSTEMS
卷 49, 期 3, 页码 461-486出版社
SPRINGER
DOI: 10.1007/s10844-017-0457-4
关键词
Semi-supervised learning; Binary classification; Multi-class classification; Decision trees; Random forests
资金
- Slovenian Research Agency [P2-0103]
- European Commission [ICT-2013-612944 MAESTRA, ICT-2013-604102 HBP]
In many real-life problems, obtaining labelled data can be a very expensive and laborious task, while unlabeled data can be abundant. The availability of labeled data can seriously limit the performance of supervised learning methods. Here, we propose a semi-supervised classification tree induction algorithm that can exploit both the labelled and unlabeled data, while preserving all of the appealing characteristics of standard supervised decision trees: being non-parametric, efficient, having good predictive performance and producing readily interpretable models. Moreover, we further improve their predictive performance by using them as base predictive models in random forests. We performed an extensive empirical evaluation on 12 binary and 12 multi-class classification datasets. The results showed that the proposed methods improve the predictive performance of their supervised counterparts. Moreover, we show that, in cases with limited availability of labeled data, the semi-supervised decision trees often yield models that are smaller and easier to interpret than supervised decision trees.
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