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.

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Title Modified AHP for gene selection and cancer classification using type-2 fuzzy logic
Author(s) Nguyen, ThanhORCID iD for Nguyen, Thanh
Nahavandi, SaeidORCID iD for Nahavandi, Saeid
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
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