Constrained Fuzzy Hierarchical Analysis for Portfolio Selection Under Higher Moments

Nguyen, Thanh; Gordon-Brown, Lee

doi:10.1109/tfuzz.2011.2181520

Cited by 41 publications

(15 citation statements)

References 27 publications

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“…The AHP method as broadly applied in complex multicriteria decision making is often performed with a tree structure of criteria and sub-criteria [22]. Due to the nature of the criteria selected here, the tree structure has three levels of hierarchies as illustrated in Fig.…”

Section: F a Novel Gene Selection By Modified Ahpmentioning

confidence: 99%

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Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic

Nguyen

Nahavandi

2016

IEEE Trans. Fuzzy Syst.

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Abstract-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 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 also of all other classifiers. 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.

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Section: F a Novel Gene Selection By Modified Ahpmentioning

confidence: 99%

“…Conventional applications of hierarchical analysis often draw on the Saaty rating scale [1,9] and rough ratios, e.g. 1, 3, 5, 7, 9 to build pairwise comparison matrices [16,22]. In this research, we propose the scale [1,10] for ranking importance or significance of a gene compared with other genes.…”

Section: F a Novel Gene Selection By Modified Ahpmentioning

confidence: 99%

Modified AHP for Gene Selection and Cancer Classification Using Type-2 Fuzzy Logic

Nguyen

Nahavandi

2016

IEEE Trans. Fuzzy Syst.

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“…t-test, entropy, ROC, Wilcoxon, and SNR. The AHP method as broadly applied in complex multi-criteria decision making is often performed with a tree structure of criteria and sub-criteria [15]. Due to the nature of the criteria selected here, the tree structure has three levels of hierarchies as illustrated in Fig.…”

Section: A Novel Gene Selection By Modified Ahp (Mahp)mentioning

confidence: 99%

A novel aggregate gene selection method for microarray data classification

Nguyen

Khosravi

Creighton

et al. 2015

Pattern Recognition Letters

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“…The AHP method as broadly applied in complex multi-criteria decision making is often performed with a tree structure of criteria and sub-criteria [20]. Due to the nature of the criteria selected here, the tree structure has three levels of hierarchies as illustrated in Fig.…”

Section: A Novel Gene Selection By Modified Ahpmentioning

confidence: 99%