Preprocessor Agent Approach to Knowledge Discovery Using Zero-R Algorithm

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.