Improving classification performance for heterogeneous cancer gene expression data

Benny Y M Fung, Vincent To Yee Ng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

In our previous work, we proposed the "impact factors" (IFs) to measure the symmetric errors in different microarray experiments, and integrated the IFs to the Golub and Slonim (GS) and k-nearest neighbors (kNN) classifiers. In this paper, we perform experiments with different cancer types, which are lung adenocarcinomas and prostate cancer, to further validate the efficiency and effectiveness of the IFs integrations in terms of measurements of classification accuracy, sensitivity and specificity. For both cancer types, the IFs integrations can be successfully improved on the classification performance.
Original languageEnglish
Title of host publicationInternational Conference on Information Technology
Subtitle of host publicationCoding Computing, ITCC 2004
Pages131-132
Number of pages2
Volume2
DOIs
Publication statusPublished - 6 Jul 2004
EventInternational Conference on Information Technology: Coding Computing, ITCC 2004 - Las Vegas, NV, United States
Duration: 5 Apr 20047 Apr 2004

Conference

ConferenceInternational Conference on Information Technology: Coding Computing, ITCC 2004
CountryUnited States
CityLas Vegas, NV
Period5/04/047/04/04

ASJC Scopus subject areas

  • Engineering(all)

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