Modified AHP for gene selection and cancer classification using type-2 fuzzy logic Nguyen, Thanh and Nahavandi, Saeid 2016, Modified AHP for gene selection and cancer classification using type-2 fuzzy logic, IEEE transactions on fuzzy systems, vol. 24, no. 2, pp. 273-287, doi: 10.1109/TFUZZ.2015.2453153. Attached Files Name Description MIMEType Size Downloads Title Modified AHP for gene selection and cancer classification using type-2 fuzzy logic Author(s) Nguyen, Thanh orcid.org/0000-0001-9709-1663 Nahavandi, Saeid orcid.org/0000-0002-0360-5270 Journal name IEEE transactions on fuzzy systems Volume number 24 Issue number 2 Start page 273 End page 287 Total pages 15 Publisher IEEE Place of publication Piscataway, N.J. Publication date 2016-04 ISSN 1063-6706 Keyword(s) analytic hierarchy process (AHP) fuzzy c-means clustering gene expression levels gene selection interval type-2 fuzzy logic microarray data classification Summary This paper proposes a modification to the analytic hierarchy process (AHP) to select the most informative genes that serve as inputs to an interval type-2 fuzzy logic system (IT2FLS) for cancer classification. Unlike the conventional AHP, the modified AHP allows us to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test, and signal-to-noise ratio. The IT2FLS is introduced for the classification task due to its great ability for handling nonlinear, noisy, and outlier data, which are common problems in cancer microarray gene expression profiles. An unsupervised learning strategy using the fuzzy c-means clustering is employed to initialize parameters of the IT2FLS. Other classifiers such as multilayer perceptron network, support vector machine, and fuzzy ARTMAP are also implemented for comparisons. Experiments are carried out on three well-known microarray datasets: diffuse large B-cell lymphoma, leukemia cancer, and prostate. Rather than the traditional cross validation, leave-one-out cross-validation strategy is applied for the experiments. Results demonstrate the performance dominance of the IT2FLS against the competing classifiers. More noticeably, the modified AHP improves the classification performance not only of the IT2FLS but of all other classifiers as well. Accordingly, the proposed combination between the modified AHP and IT2FLS is a powerful tool for cancer classification and can be implemented as a real clinical decision support system that is useful for medical practitioners. Language eng DOI 10.1109/TFUZZ.2015.2453153 Field of Research 080108 Neural, Evolutionary and Fuzzy Computation 0801 Artificial Intelligence And Image Processing 0906 Electrical And Electronic Engineering 0102 Applied Mathematics Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences HERDC Research category C1 Refereed article in a scholarly journal ERA Research output type C Journal article Copyright notice ©2015, IEEE Persistent URL http://hdl.handle.net/10536/DRO/DU:30084985 Document type: Journal Article Collections: Institute for Intelligent Systems Research and Innovation (IISRI) 2018 ERA Submission GTP Research Related Links Link Description Connect to published version Go to link with your DU access privileges     Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved. Versions Version Filter Type Tue, 19 Jul 2016, 15:13:09 EST Tue, 19 Jul 2016, 15:14:55 EST Wed, 20 Jul 2016, 05:02:17 EST Wed, 20 Jul 2016, 06:10:45 EST Thu, 04 Aug 2016, 16:46:41 EST Thu, 04 Aug 2016, 16:47:21 EST Wed, 12 Oct 2016, 15:42:35 EST Wed, 01 Mar 2017, 08:15:23 EST Filtered Full Citation counts:   Cited 45 times in TR Web of Science Cited 59 times in Scopus Search Google Scholar Access Statistics: 546 Abstract Views, 2 File Downloads  -  Detailed Statistics Created: Tue, 19 Jul 2016, 15:14:55 EST

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