An Interpretable Semi-supervised Classifier using Rough Sets for Amended Self-labeling

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

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

Abstract

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
interpretable self-labeling grey-box classifier that uses a black
box to estimate the missing class labels and a white box to make
the final predictions. We propose a Rough Set based approach for
amending the self-labeling process. We compare its performance
to the vanilla version of our self-labeling grey-box and the
use of a confidence-based amending. In addition, we introduce
some measures to quantify the interpretability of our model.
The experimental results suggest that the proposed amending
improves accuracy and interpretability of the self-labeling grey-box,
thus leading to superior results when compared to state-of-the-
art semi-supervised classifiers.
Original languageEnglish
Title of host publicationProceedings of the 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
PublisherIEEE
Pages1-8
ISBN (Electronic)978-1-7281-6932-3
ISBN (Print)978-1-7281-6933-0
DOIs
Publication statusPublished - 2020
Event IEEE World Congress on Computational Intelligence (WCCI) 2020: IEEE International Conference on Fuzzy Systems - Glasgow, United Kingdom
Duration: 19 Jul 2020 → …
https://wcci2020.org/

Conference

Conference IEEE World Congress on Computational Intelligence (WCCI) 2020
Abbreviated titleFUZZ-IEEE
CountryUnited Kingdom
CityGlasgow
Period19/07/20 → …
Internet address

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