Fuzzy-Rough Set based Semi-Supervised Learning

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Much work has been carried out in the area of fuzzy-rough sets for supervised learning. However, very little has been accomplished for the unsupervised or semi-supervised tasks. For many real-word applications, it is often expensive, time-consuming and difficult to obtain labels for all data objects. This often results in large quantities of data which may only have very few labelled data objects. This paper proposes a novel fuzzy-rough based semi-supervised self-learning or self-training approach for the assignment of labels to unlabelled data. Unlike other semi-supervised approaches, the proposed technique requires no subjective thresholding or domain information. An experimental evaluation is performed on artificial data and also applied to a real-world mammographic risk assessment problem with encouraging results.
Original languageEnglish
Title of host publication2011 IEEE International Conference on Fuzzy Systems (FUZZ)
Number of pages7
ISBN (Electronic)978-1-4244-7316-8
Publication statusPublished - 06 Jul 2011
EventFuzzy Systems - Taipei, Taiwan
Duration: 27 Jun 201130 Jun 2011
Conference number: 20


ConferenceFuzzy Systems
Abbreviated titleFUZZ-IEEE-2011
Period27 Jun 201130 Jun 2011


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