The effect of the characteristics of the dataset on the selection stability

Salem Alelyani, Huan Liu, Lei Wang

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

17 Scopus citations

Abstract

Feature selection is an effective technique to reduce the dimensionality of a data set and to select relevant features for the domain problem. Recently, stability of feature selection methods has gained increasing attention. In fact, it has become a crucial factor in determining the goodness of a feature selection algorithm besides the learning performance. In this work, we conduct an extensive experimental study using verity of data sets and different well-known feature selection algorithms in order to study the behavior of these algorithms in terms of the stability.

Original languageEnglish (US)
Title of host publicationProceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
Pages970-977
Number of pages8
DOIs
StatePublished - Dec 1 2011
Event23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011 - Boca Raton, FL, United States
Duration: Nov 7 2011Nov 9 2011

Publication series

NameProceedings - International Conference on Tools with Artificial Intelligence, ICTAI
ISSN (Print)1082-3409

Other

Other23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011
CountryUnited States
CityBoca Raton, FL
Period11/7/1111/9/11

Keywords

  • Data distribution
  • Dimensionality reduction
  • Feature selection algorithms
  • Jaccard index
  • Sample size
  • Stability

ASJC Scopus subject areas

  • Software
  • Artificial Intelligence
  • Computer Science Applications

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  • Cite this

    Alelyani, S., Liu, H., & Wang, L. (2011). The effect of the characteristics of the dataset on the selection stability. In Proceedings - 2011 23rd IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2011 (pp. 970-977). [6103458] (Proceedings - International Conference on Tools with Artificial Intelligence, ICTAI). https://doi.org/10.1109/ICTAI.2011.167