Document Type
Thesis
Date of Award
Summer 8-31-2004
Degree Name
Master of Science in Computational Biology - (M.S.)
Department
College of Computing Sciences
First Advisor
Qun Ma
Second Advisor
Usman W. Roshan
Third Advisor
Frank Y. Shih
Abstract
Support Vector Machine (SVM) is a supervised machine learning technique being widely used in multiple areas of biological analysis including microarray data analysis. SlimSVM has been developed with the intention of replacing OSU SVM as the classification component of GenoIterSVM in order to make it independent of other SVM packages. GenolterSVM, developed by Dr. Marc Ma, is a SVM implementation with an iterative refinement algorithm for improved accuracy of classification of genotype microarray data. SlimSVM is an object-oriented, modular, and easy-to-use implementation written in C++. It supports dot (linear) and polynomial (non-linear) kernels. The program has been tested with artificial non-biological and microarray data. Testing with microarray data was performed to observe how SlimSVM handles medium-sized data files (containing thousands of data points) since it would ultimately be used to analyze them. The results were compared to those of LIBSVM, a leading SVM software, and the comparison demonstrates that implementation of SlimS VM was carried out accurately.
Recommended Citation
Karmaker, Avik, "SLIMSVM : a simple implementation of support vector machine for analysis of microarray data" (2004). Theses. 577.
https://digitalcommons.njit.edu/theses/577
