New Feature Splitting Criteria for Co-training Using Genetic Algorithm Optimization

Ahmed Salaheldin*, Neamat El Gayar

*Corresponding author for this work

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

12 Citations (Scopus)


Often in real world applications only a small number of labeled data is available while unlabeled data is abundant. Therefore, it is important to make use of unlabeled data. Co-training is a popular semi-supervised learning technique that uses a small set of labeled data and enough unlabeled data to create more accurate classification models. A key feature for successful co-training is to split the features among more than one view. In this paper we propose new splitting criteria based on the confidence of the views, the diversity of the views, and compare them to random and natural splits. We also examine a previously proposed artificial split that maximizes the independence between the views, and propose a mixed criterion for splitting features based on both the confidence and the independence of the views. Genetic algorithms are used to choose the splits which optimize the independence of the views given the class, the confidence of the views in their predictions, and the diversity of the views. We demonstrate that our proposed splitting criteria improve the performance of co-training.

Original languageEnglish
Title of host publicationMultiple Classifier Systems. MCS 2010
Number of pages11
ISBN (Electronic)9783642121272
ISBN (Print)9783642121265
Publication statusPublished - 2010
Event9th International Workshop on Multiple Classifier Systems 2010 - Cairo, Egypt
Duration: 7 Apr 20109 Apr 2010

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference9th International Workshop on Multiple Classifier Systems 2010
Abbreviated titleMCS 2010

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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