Abstract
This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.
Highlights
A large number of genes cannot be possibly analysed by traditional methods
We propose a novel method for gene selection by advancing the traditional analytic hierarchy process (AHP) to adopt quantitative criteria that include statistics of t-test, entropy, receiver operating characteristic (ROC), Wilcoxon, and Signal to noise ratio (SNR)
Among the investigated gene selection methods, the AHP criterion exhibits supremacy compared to t-test, entropy, ROC, Wilcoxon and SNR methods. It is understandable as our proposed AHP is capable of deriving subsets of most informative genes as an elite collection of those raised by individual methods
Summary
A large number of genes cannot be possibly analysed by traditional methods. DNA microarray is a technique that enables researchers to analyse the expression of many genes speedily. I.e. t-test, entropy, ROC, Wilcoxon, SNR and our proposed modified AHP presented, are carried out to select most discriminative genes for classification. The average LOOCV accuracy across different numbers of genes of the four classification methods, i.e. MLP, SVM, FARTMAP and FSAM is reported in Table 5 and 6 for the DLBCL and leukemia datasets respectively.
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