Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/2301
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dc.contributor.authorKumar, Mukesh-
dc.contributor.authorRath, S K-
dc.date.accessioned2015-04-17T15:27:38Z-
dc.date.available2015-04-17T15:27:38Z-
dc.date.issued2014-08-21-
dc.identifier.citationInternational Scholarly Research Notices Volume 2014, 18 pages.2014,Article ID 769159en_US
dc.identifier.urihttp://dx.doi.org/10.1155/2014/769159-
dc.identifier.urihttp://hdl.handle.net/2080/2301-
dc.description.abstractThe DNA microarray classification technique has gained more popularity in both research and practice. In real data analysis, such as microarray data, the dataset contains a huge number of insignificant and irrelevant features that tend to lose useful information. Classes with high relevance and feature sets with high significance are generally referred for the selected features, which determine the samples classification into their respective classes. In this paper, kernel fuzzy inference system (K-FIS) algorithm is applied to classify the microarray data (leukemia) using t-test as a feature selection method. Kernel functions are used to map original data points into a higher-dimensional (possibly infinite-dimensional) feature space defined by a (usually nonlinear) function through a mathematical process called the kernel trick. This paper also presents a comparative study for classification using K-FIS along with support vector machine (SVM) for different set of features (genes). Performance parameters available in the literature such as precision, recall, specificity, F-measure, ROC curve, and accuracy are considered to analyze the efficiency of the classification model. From the proposed approach, it is apparent that K-FIS model obtains similar results when compared with SVM model. This is an indication that the proposed approach relies on kernel function.en_US
dc.language.isoenen_US
dc.publisherHindawi Publishing Corporationen_US
dc.subjectDNA microarrayen_US
dc.subjectkernel fuzzyen_US
dc.subjectkernel tricken_US
dc.subjectinference systemen_US
dc.titleClassification of Microarray Data Using Kernel Fuzzy Inference Systemen_US
dc.typeArticleen_US
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Journal Articles

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