A semi-supervised learning approach for soft labeled data

Mohamed M. El-Zahhar, Neamat F. El-Gayar

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

2 Citations (Scopus)

Abstract

In some machine learning applications using soft labels is more useful and informative than crisp labels. Soft labels indicate the degree of membership of the training data to the given classes. Often only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. In this paper we propose an approach for Fuzzy-Input Fuzzy-Output classification in which the classifier can learn with soft-labeled data and can also produce degree of belongingness to classes as an output for each pattern. Particularly, we investigate the case where only a few soft labels are available and data can be represented by different views. We investigate two semi-supervised multiple classifier frameworks for this classification purpose. Results show that semi supervised multiple classifiers can improve the performance of fuzzy classification by making use of the unlabeled data.

Original languageEnglish
Title of host publication2010 10th International Conference on Intelligent Systems Design and Applications
PublisherIEEE
Pages1136-1141
Number of pages6
ISBN (Electronic)9781424481361
ISBN (Print)9781424481347
DOIs
Publication statusPublished - 13 Jan 2011
Event10th International Conference on Intelligent Systems Design and Applications 2010 - Cairo, Egypt
Duration: 29 Nov 20101 Dec 2010

Conference

Conference10th International Conference on Intelligent Systems Design and Applications 2010
Abbreviated titleISDA'10
CountryEgypt
CityCairo
Period29/11/101/12/10

Keywords

  • Co-training
  • Fuzzy classifier
  • Multiple classifiers
  • Semi-supervised learning
  • Soft label

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Hardware and Architecture

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  • Cite this

    El-Zahhar, M. M., & El-Gayar, N. F. (2011). A semi-supervised learning approach for soft labeled data. In 2010 10th International Conference on Intelligent Systems Design and Applications (pp. 1136-1141). IEEE. https://doi.org/10.1109/ISDA.2010.5687034