Effective
Classification with Improved Quick Reduct For Medical
Database Using Rough System
K.Thangavel1,
M.Karnan2*,
P.Jeganathan2,
A.Petha
lakshmi3
1Department
of Computer Science, Periyar University, Salem,Tamil
Nadu, India.
2*
Department of Computer Science and Applications,
Gandhigram Rural Institute-Deemed University,
Gandhigram-624 302, Tamil Nadu, India.
3Department of Computer Science, M.V.M Government Arts College (W), Dindigul-624 001, Tamil Nadu, India.
Abstract
The volume of
data being generated nowadays is increasing at
phenomenal rate. Extracting useful knowledge from such
data collections is an important and challenging issue.
A promising technique is the Rough set theory, a new
mathematical approach to data analysis based on
classification of objects of interest into similarity
classes, which are indiscernible with respect to some
features. This theory offers two fundamental concepts:
reduct and core. In this paper, Quick Reduct and the
proposed Improved Quick Reduct Algorithms are first
presented, followed by the c4.5 approach for rule
induction. Some experiment results are also given. The
redundant attributes are eliminated in order to
generate the effective reduct set (i.e., reduced set of
necessary attributes) or to construct the core of the
attribute set. This paper analyses the efficiency of
the proposed Improved Quickreduct Algorithm against the
standard Quick Reduct Algorithm. The experimental works
are carried out on medical data sets of UCI machine
learning repository and the Human Immuno deficiency
Virus(HIV)
data set.
Key
words: Rough set theory, Data
mining, Knowledge discovery, Feature selection,
Quickreduct, Improved Quickreduct.
Biography:
 |
 Palanichamy
Jaganathan, received the master
degree of science in Physics and another master
degree in Computer Applications from the Madurai
Kamaraj University, Madurai, Tamilnadu, India,
in 1993, and 2001, respectively and currently
pursuing the Ph.D., degree in Computer Science
and Applications from the Department of Computer
Science and Applications, Gandhigram Rural
Institute-Deemed University, Gandhigram, India.
He joined the PSNA College of Engineering and
Technology, Tamilnadu, India in 1995 and has
been an Assistant Professor in the Department of
Computer Applications. He has been in teaching
since 1993. His areas of interest include Data
mining, Genetic Algorithms, Neural networks,
Machine Learning and Artificial intelligence.
|
 |
Palanichamy
Jaganathan, received the master
degree of science in Physics and another master
degree in Computer Applications from the Madurai
Kamaraj University, Madurai, Tamilnadu, India,
in 1993, and 2001, respectively and currently
pursuing the Ph.D., degree in Computer Science
and Applications from the Department of Computer
Science and Applications, Gandhigram Rural
Institute-Deemed University, Gandhigram, India.
He joined the PSNA College of Engineering and
Technology, Tamilnadu, India in 1995 and has
been an Assistant Professor in the Department of
Computer Applications. He has been in teaching
since 1993. His areas of interest include Data
mining, Genetic Algorithms, Neural networks,
Machine Learning and Artificial intelligence.
|
 |
PETHALAKSHMI
ANNAMALAI,
received the
Master of Computer Science degree from Alagappa
University, Tamil Nadu, India, in 1988 and the
Master of Philosophy in Computer Science from
Mother Teresa Women’s University, Tamil Nadu,
India, in 2000. She is working as Selection
Grade Lecturer, Department of Computer Science,
M.V.M. Govt. Arts College (w), Dindigul, Tamil
Nadu, India, and currently she is the full-time
research scholar in the Department of Computer
Science, Mother Teresa Women’s University, Tamil
Nadu, India. Her areas of interests include
fuzzy, rough set and neural network.
|
 |
KARNAN MARCUS,
received the Master of Computer Science and
Engineering Degree from Computer Science and
Engineering Department, from Government College
of Engineering, Manonmaniam Sundaranar
University, Tamil Nadu, India, in 2000.
Currently he is working as Professor, Department
of Computer Science & Engineering, HINDUSTHAN
College of Engineering & Technology, Tamil Nadu,
India. And doing part-time research in the
Department of computer Science and Applications,
Gandhigram Rural Institute-Deemed University,
Tamil Nadu, India. His area of interests
includes medical image processing, artificial
intelligence, neural network, genetic algorithm,
pattern recognition and fuzzy logic.
|
BibTex:
@ARTICLE{P1160613001,
AUTHOR
= {
K.Thangavel and P. Jaganathan and A. Pethalakshmi and
M.Karnan },
TITLE
= {Effective
Classification with Improved Quick Reduct For
Medical Database Using Rough System},
JOURNAL = {ICGST International
Journal on Bioinformatics and Medical Engineering},
YEAR = {2005},
VOLUME={05},
ISSUE= {I},
PAGAES={7--14}
}
( Full
Paper, 566 KB)
|
