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Title: Classification of Multiple Cancer Types by Support Vector Machines using Gene Expression Data
Authors: Meena, D. K.
Keywords: Computer Science & Engineering
Issue Date: 2015
Abstract: Cancer is one of the atrocious diseases found in most of the living organism, which is one of the challenging studies for scientist in 21st century. Basically Cancer is charac- terized by an abnormal, uncontrolled growth that may destroy and invade adjacent healthy body tissues or elsewhere in the body. This project deals with the advanced and developed methodology for cancer classification using Support Vector Machine (SVM) for microar- ray gene expression data. High-density DNA microarray measures the activities of several thousand genes simultaneously and the gene expression profiles have been used for the can- cer classification. This new approach promises to give better therapeutic measurements to cancer patients by diagnosing cancer types with improved accuracy. The support vec- tor machine (SVM) has exploded in popularity within the machine learning literature and, more recently, has received increasing attention from the statistics community as well. The Support Vector Machine (SVM) is one of the classification methods successfully applied to the cancer diagnosis problems. However, its optimal extension to more than two classes was not obvious, which might impose limitations in its application to multiple tumor types. Multi-category SVM technique overcomes the previous classification methodology by means of time consumption and by giving best accuracy rate.
Appears in Collections:01. CSE

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