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Adaptive strategy operators based GA for rule discovery

  • T. ShobhaEmail author
  • R. J. Anandhi
Original Research
  • 5 Downloads

Abstract

A new variant of genetic algorithm, which provides equal opportunity for all parent solution to produce the offspring solution, has been applied in discovery of classification rules from continuous datasets. The main objective of proposed algorithm is used to discover classification rule with three measures like accuracy, coverage (completeness) and comprehensibility, using which easily understandable, accurate and comprehensible rules can be generated. A new process has been defined to simplify the generated rules by reducing the features dimension, according to their role in the success of discovering rules. Adaptive approach for crossover and mutation operations has been applied to handle the exploration and exploitation in dynamic manner. Algorithm has been tested on UCI benchmark dataset. The results show the better classification accuracy and optimal selection of features. It is also observed that, proposed solution generates rules which are easy to handle and does not require computational machine for applications use.

Keywords

Adaptive operators Classification Genetic algorithm Rule discovery 

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Copyright information

© Bharati Vidyapeeth's Institute of Computer Applications and Management 2019

Authors and Affiliations

  1. 1.Department of CSEThe Oxford College of EngineeringBangaloreIndia
  2. 2.Department of ISENew Horizon College of EngineeringBangaloreIndia

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