Gene function classification using fuzzy K-Nearest Neighbor approach

Dan Li, Jitender S. Deogun, Kefei Wang

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

10 Scopus citations

Abstract

Prediction of gene function is a classification problem. Given its simplicity and relatively high accuracy, K-Nearest Neighbor (KNN) classification has become a popular choice for many real life applications. However, traditional KNN approach has two drawbacks. First, it cannot identify classes that do not exist in the training data sets. Second, it treats all K neighbors in a similar way without consideration of the distance differences between the test instance and its neighbors. In this paper, exploiting the potential of fuzzy set theory to handle uncertainty in data sets, we develop a fuzzy KNN approach for gene function classification. Experiments show that integrating fuzzy set theory into original KNN approach improves the overall performance of the classification model.

Original languageEnglish (US)
Title of host publicationProceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007
Pages644-647
Number of pages4
DOIs
StatePublished - 2007
Event2007 IEEE International Conference on Granular Computing, GrC 2007 - San Jose, CA, United States
Duration: Nov 2 2007Nov 4 2007

Publication series

NameProceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007

Conference

Conference2007 IEEE International Conference on Granular Computing, GrC 2007
CountryUnited States
CitySan Jose, CA
Period11/2/0711/4/07

ASJC Scopus subject areas

  • Computational Theory and Mathematics
  • Computer Science Applications
  • Theoretical Computer Science

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    Li, D., Deogun, J. S., & Wang, K. (2007). Gene function classification using fuzzy K-Nearest Neighbor approach. In Proceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007 (pp. 644-647). [4403179] (Proceedings - 2007 IEEE International Conference on Granular Computing, GrC 2007). https://doi.org/10.1109/GRC.2007.4403179