Interpretable self-labeling semi-supervised classifier

Isel Grau, Dipankar Sengupta, Maria M. Garcia Lorenzo, Ann Nowé

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Semi-supervised classification refers to a type ofpattern classification problem involving both labeledand unlabeled data, where the number of labeledinstances is often significantly smaller comparedto the number of unlabeled ones. Althoughthere exist several semi-supervised classifiers withhigh performance over different tasks, most of themare complex models that do not allow explainingthe obtained outcome, thus behaving like blackboxes. In this paper, we perform a critical analysisof the interpretability of state-of-the-art semisupervisedclassification approaches. In addition,we present a self-labeling grey-box classifier thatuses a black-box to estimate the missing class labelsand an interpretable white-box to make theactual predictions. The main contribution of thismodel relies on its transparency while also beingable to outperform most state-of-the-art semisupervisedclassifiers.
Original languageEnglish
Title of host publicationProceedings of the 2nd Workshop on Explainable Artificial Intelligence
Subtitle of host publication27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence
EditorsDavid W. Aha, Trevor Darrell, Patrick Doherty, Daniele Magazzeni
Place of PublicationStockholm, Sweden
Pages52-57
Publication statusPublished - 19 Jul 2018

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  • Cite this

    Grau, I., Sengupta, D., Lorenzo, M. M. G., & Nowé, A. (2018). Interpretable self-labeling semi-supervised classifier. In D. W. Aha, T. Darrell, P. Doherty, & D. Magazzeni (Eds.), Proceedings of the 2nd Workshop on Explainable Artificial Intelligence: 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (pp. 52-57). http://home.earthlink.net/~dwaha/research/meetings/faim18-xai/