Scoring method for tumor prediction from microarray data using an evolutionary fuzzy classifier

Shinn-Ying Ho*, Chih Hung Hsieh, Kuan Wei Chen, Hui Ling Huang, Hung Ming Chen, Shinn Jang Ho

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

In this paper, we propose a novel scoring method for tumor prediction using an evolutionary fuzzy classifier which can provide accurate and interpretable information. The merits of the proposed method are threefold. 1) The score ranged in [0, 100] can further illustrate the degree of tumor status in contrast to the conventional tumor classifier. 2) The derived score system can be used as a tumor classifier using a system-suggested or human-specified threshold value. 3) The derived classifier with a compact fuzzy rule base can generate an interpretable and accurate prediction result. The effectiveness of the proposed method is evaluated and compared using two well-known datasets from microarray data and an existing tumor classifier. It is shown by computer simulation that the proposed scoring method is effective using ROC curves of classification.

Original languageEnglish
Title of host publicationAdvances in Knowledge Discovery and Data Mining - 10th Pacific-Asia Conference, PAKDD 2006, Proceedings
Pages520-529
Number of pages10
DOIs
StatePublished - 14 Jul 2006
Event10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006 - Singapore, Singapore
Duration: 9 Apr 200612 Apr 2006

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3918 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2006
CountrySingapore
CitySingapore
Period9/04/0612/04/06

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