THA-A Hybrid Approach for Rule Induction System using Rough Set Theory, Genetic Algorithm and Boolean algebra

Tribikram Pradhan; Harsh Anand; Akul Goyal

THA-A Hybrid Approach for Rule Induction System using Rough Set Theory, Genetic Algorithm and Boolean algebra

Tribikram Pradhan * §

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Harsh Anand §

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Akul Goyal §

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§ Manipal Institute of Technology, Manipal University

THA-A Hybrid Approach for Rule Induction System using Rough Set Theory, Genetic Algorithm and Boolean algebra

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Abstract

The major process of discovering knowledge in database is the extraction of rules from classes of data. One of the major obstacles in performing rule induction from training data set is the inconsistency of information about a problem domain. In order to deal with this problem, many theories and technology have been developed in recent years. Among them the most successful ones are decision tree, fuzzy set, Dempster-Shafer theory of evidence. Unfortunately, all are referring to either prior or posterior probabilities. The rough set concept proposed by Pawlak is a new mathematical approach to inconsistent, vagueness, imprecision and uncertain data. In this paper we have proposed a hybridized model THA (Training dataset on hybrid approach) which combines rough set theory, genetic algorithm and Boolean algebra for discovering certain rules and also induce probable rules from inconsistent information. The experimental result shows that the projected method induced maximal generalized rules efficiently. The hybridized model was validated using the data obtained from observational study.

References

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Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Tribikram Pradhan, Harsh Anand, Akul Goyal. 2014. "THA-A Hybrid Approach for Rule Induction System using Rough Set Theory, Genetic Algorithm and Boolean algebra". Global Journal of Research in Engineering - I: Numerical Methods GJRE-I Volume 14 (GJRE Volume 14 Issue I1).

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GJRE Volume 14 Issue I1

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Journal Specifications

Crossref Journal DOI 10.17406/gjre

Print ISSN 0975-5861

e-ISSN 2249-4596

Keywords

Boolean algebra Crossover Genetic Algorithm Mutation rough set theory

Classification

GJRE-I Classification FOR Code: 010101

230101p

Submission ReceivedDecember 14, 2013
Peer Review Double Blind
Handling Editor
Accepted January 3, 2014
Published January 15, 2014

Version of record

v1.2

Issue date

June 30, 2014

Language

English

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Article Matrices

Total Score: 103

Country: India

Subject: Global Journal of Research in Engineering - I: Numerical Methods

Authors: Tribikram Pradhan, Harsh Anand, Akul Goyal (PhD/Dr. count: 0)

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