A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2020; you can also visit the original URL.
The file type is
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 andoi:10.1109/fuzz48607.2020.9177549 dblp:conf/fuzzIEEE/GrauSLN20 fatcat:hy7sqc3ibrc7pdnr3rt5airitu