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An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling
2020
2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Semi-supervised classifiers combine labeled and unlabeled data during the learning phase in order to increase classifier's generalization capability. However, most successful semi-supervised classifiers involve complex ensemble structures and iterative algorithms which make it difficult to explain the outcome, thus behaving like black boxes. Furthermore, during an iterative self-labeling process, mistakes can be propagated if no amending procedure is used. In this paper, we build upon an
doi:10.1109/fuzz48607.2020.9177549
dblp:conf/fuzzIEEE/GrauSLN20
fatcat:hy7sqc3ibrc7pdnr3rt5airitu