Data mining and multiagent approach has been used successfully in the development of large complex systems. Agents are used to perform some action or activity on behalf of a user of a computer system. The study proposes an agent based algorithm PrePZero-r using Zero-R algorithm in Weka. Algorithms are powerful technique for solution of various combinatorial or optimization problems. Zero-R is a simple and trivial classifier, but it gives a lower bound on the performance of a given dataset which should be significantly improved by more complex classifiers. The Proposed Algorithm called PrePZero-r has significantly reduced time taken to build the model than Zero-R algorithm by removing the Lower Bound Values 0 while preprocessing and comparing the result with class values. Also proposed study introduced new factor “Accuracy (1-e)” for each individual attribute.
%0 Journal Article
%1 IJACSA.2011.021212
%A Inamdar S A Narangale S.M., G N Shinde
%D 2011
%J International Journal of Advanced Computer Science and Applications(IJACSA)
%K Bound Class Data Lower Value; Zero-R algorithm; mining; values.
%N 12
%T Preprocessor Agent Approach to Knowledge Discovery Using Zero-R Algorithm
%U http://ijacsa.thesai.org/
%V 2
%X Data mining and multiagent approach has been used successfully in the development of large complex systems. Agents are used to perform some action or activity on behalf of a user of a computer system. The study proposes an agent based algorithm PrePZero-r using Zero-R algorithm in Weka. Algorithms are powerful technique for solution of various combinatorial or optimization problems. Zero-R is a simple and trivial classifier, but it gives a lower bound on the performance of a given dataset which should be significantly improved by more complex classifiers. The Proposed Algorithm called PrePZero-r has significantly reduced time taken to build the model than Zero-R algorithm by removing the Lower Bound Values 0 while preprocessing and comparing the result with class values. Also proposed study introduced new factor “Accuracy (1-e)” for each individual attribute.
@article{IJACSA.2011.021212,
abstract = {Data mining and multiagent approach has been used successfully in the development of large complex systems. Agents are used to perform some action or activity on behalf of a user of a computer system. The study proposes an agent based algorithm PrePZero-r using Zero-R algorithm in Weka. Algorithms are powerful technique for solution of various combinatorial or optimization problems. Zero-R is a simple and trivial classifier, but it gives a lower bound on the performance of a given dataset which should be significantly improved by more complex classifiers. The Proposed Algorithm called PrePZero-r has significantly reduced time taken to build the model than Zero-R algorithm by removing the Lower Bound Values 0 while preprocessing and comparing the result with class values. Also proposed study introduced new factor “Accuracy (1-e)” for each individual attribute.},
added-at = {2014-02-21T08:00:08.000+0100},
author = {{Inamdar S A Narangale S.M.}, G N Shinde},
biburl = {https://www.bibsonomy.org/bibtex/2f6ef77179788e26866c9c641d7904afc/thesaiorg},
interhash = {0598311cf7b6d4c6852812b8f7fd34e6},
intrahash = {f6ef77179788e26866c9c641d7904afc},
journal = {International Journal of Advanced Computer Science and Applications(IJACSA)},
keywords = {Bound Class Data Lower Value; Zero-R algorithm; mining; values.},
number = 12,
timestamp = {2014-02-21T08:00:08.000+0100},
title = {{Preprocessor Agent Approach to Knowledge Discovery Using Zero-R Algorithm}},
url = {http://ijacsa.thesai.org/},
volume = 2,
year = 2011
}