The following information was submitted:
Transactions: WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE
Transactions ID Number: 52-388
Full Name: Sasikala subramaniam
Position: Assistant Professor
Age: ON
Sex: Female
Address: 92.Sri Rajarajeswari Mills,Covai Road,Pollachi,Tamilnadu
Country: INDIA
Tel: 9791333877
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Fax:
E-mail address: sasivenkatesh04@gmail.com
Other E-mails: sasikalahod@gmail.com
Title of the Paper: Multicategory Classification Using Support Vector Machine for Microarray Gene Expression Cancer Diagnosis
Authors as they appear in the Paper: Dr.s.santhosh Baboo , S.Sasikala
Email addresses of all the authors: santhos2001@sify.com , sasivenkatesh04@gmail.com
Number of paper pages: 10
Abstract: Abstract— This paper deals with the advanced and developed methodology know for cancer multi classification using Support Vector Machine (SVM) for microarray gene expression cancer diagnosis, this is used for directing multicategory classification problems in the cancer diagnosis area. SVMs are an appropriate new technique for binary classification tasks, which is related to and contain elements of non-parametric applied statistics, neural networks and machine learning. SVMs can generate accurate and robust classification results on a sound theoretical basis, even when input data are non-monotone and non-linearly separable. The performance of SVM is evaluated for the multicategory classification on benchmark microarray data sets for cancer diagnosis, namely, the SRBCT Data set. The results indicate that SVM produces comparable or better classification accuracies when the data given as input are preprocessed. SVM delivers high performance with reduced training time !
and implementation complexity is less when compared to artificial neural networks methods like conventional back-propagation ANN and Linder's SANN.
Keywords: Keyword- SVM, ANOVA, Cancer Classification and Gene Expression
EXTENSION of the file: .doc
Special (Invited) Session:
Organizer of the Session:
How Did you learn about congress: Gene selection and cancer classification
IP ADDRESS: 115.248.107.114
