By Topic

An Effective Classification Model for Cancer Diagnosis Using Micro Array Gene Expression Data

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Saravanan, V. ; Dept. of Comput. Applic., Karunya Univ., Coimbatore ; Mallika, R.

Data mining algorithms are commonly used for cancer classification. Prediction models were widely used to classify cancer cells in human body. This paper focuses on finding small number of genes that can best predict the type of cancer. From the samples taken from several groups of individuals with known classes, the group to which a new individual belongs to is determined accurately. The paper uses a classical statistical technique for gene ranking and SVM classifier for gene selection and classification. The methodology was applied on two publicly available cancer databases. SVM one-against- all and one-against-one method were used with two different kernel functions and their performances are compared and promising results were achieved.

Published in:

Computer Engineering and Technology, 2009. ICCET '09. International Conference on  (Volume:1 )

Date of Conference:

22-24 Jan. 2009