Complementary feature splits for co-training

Ahmed Salaheldin, Neamat El-Gayar

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

4 Citations (Scopus)

Abstract

In many data mining and machine learning applications, data may be easy to collect. However, labeling the data is often expensive, time consuming or difficult. Such applications give rise to semi-supervised learning techniques that combine the use of labelled and unlabelled data. Co-training is a popular semi-supervised learning algorithm that depends on splitting the features of a data set into two redundant and independent views. In many cases however such sets of features are not naturally present in the data or are unknown. In this paper we test feature splitting methods based on maximizing the confidence and the diversity of the views using genetic algorithms, and compare their performance against random splits. We also propose a new criterion that maximizes the complementary nature of the views. Experimental results on six different data sets show that our optimized splits enhance the performance of co-training over random splits and that the complementary split outperforms the confidence, diversity and random splits.

Original languageEnglish
Title of host publication2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA)
PublisherIEEE
Pages1303-1308
Number of pages6
ISBN (Electronic)9781467303828
ISBN (Print)9781467303811
DOIs
Publication statusPublished - 24 Sep 2012
Event11th International Conference on Information Science, Signal Processing and their Applications 2012 - Montreal, QC, Canada
Duration: 2 Jul 20125 Jul 2012

Conference

Conference11th International Conference on Information Science, Signal Processing and their Applications 2012
Abbreviated titleISSPA 2012
CountryCanada
CityMontreal, QC
Period2/07/125/07/12

ASJC Scopus subject areas

  • Computer Science Applications
  • Signal Processing

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  • Cite this

    Salaheldin, A., & El-Gayar, N. (2012). Complementary feature splits for co-training. In 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA) (pp. 1303-1308). IEEE. https://doi.org/10.1109/ISSPA.2012.6310494