Classification of Denver System of Chromosomes using Similarity Classifier guided by OWA Operators

ISSN: 2212-392X (Online)
ISSN: 1574-8936 (Print)


Volume 9, 5 Issues, 2014


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Alessandro Giuliani
Istituto Superiore di Sanitá (Italian NIH) Environment and Health Dept
Roma
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Classification of Denver System of Chromosomes using Similarity Classifier guided by OWA Operators

Author(s): Sivaramakrishnan Rajaraman and Arun Chokkalingam

Affiliation: Department of Biomedical Engineering SSN College of Engineering Kalavakkam 603110 India.

Abstract

This paper proposes a Similarity Classifier guided by Ordered Weighted Averaging (OWA) operators to classify ‘Denver System’ of human chromosomes. A chromosome is assigned to one of the seven groups from A to G, based on the ‘Denver System’ of classification of chromosomes. Chromosomes within the group are difficult to identify, possessing almost identical characteristics including the length and centromere position. The proposed method is applied to the chromosome dataset and the similarity values using the generalized Lukasiewicz structure are calculated. The similarity values are aggregated with the help of Ordered Weighted Averaging (OWA) operators with a Regular Increasing Monotone (RIM) quantifier and the chromosome is assigned to that group for which it has the highest value of similarity. This work evaluates the performance of classifiers including Naive Bayes, Quadratic classifier and a novel, unsupervised, Fuzzy Similarity Classifier guided by OWA Operators, in classifying the Denver Group of chromosomes. A fundamental review on fuzzy similarity based classification is also presented. Experimental results clearly demonstrates that the proposed Fuzzy Similarity Classifier guided by OWA Operators with a RIM quantifier, produces the best classification results with a mean classification accuracy of 96.0028% and variance of 5.0871e-005. Statistical testing with one-way Analysis of Variance (ANOVA) at 95% and 99% level of confidence and Tukey’s post-hoc analysis is performed to validate the selection of the classifier. The proposed Fuzzy Similarity Classifier gives the most promising classification results and can be applied to any large scale biomedical data and other applications.


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Article Details

Volume: 8
First Page: 1
Page Count: 1
DOI: 10.2174/1574893608666131231231238
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