Rule-Based Classification using Multi-Soft Set Theory
Santhosh Kottam1, Varghese Paul2

1Santhosh Kottam*, Research Scholar, Research and Development Centre, Bharathiyar University, Coimbatore, India.
2Dr. Varghese Paul, Professor & HOD, Department of Information Technology, Cochin University of Science & Technology, Thrikkakara, Kochi, India.
Manuscript received on January 26, 2020. | Revised Manuscript received on February 05, 2020. | Manuscript published on February 30, 2020. | PP: 2269-2276 | Volume-9 Issue-3, February 2020. | Retrieval Number: C5908029320/2020©BEIESP | DOI: 10.35940/ijeat.C5908.029320
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Abstract: One of the important data mining functionality is classification. Presently, different methods exist for implementing classification. Rule-based classification using decision tree induction method is a conventional and simple method for identifying an unknown class of a given object. This method has a set of demerits and to remove these demerits, we depend on a soft computing tool which is known as soft set theory. One branch of soft set theory is called – multi soft theory- and it has a wide range of applications in the area of classification. We made a certain alteration in the rule-based classification using decision tree induction method by applying multi soft set theory. These changes will simplify the difficulties of the rule-based classification using decision tree induction method. The first two sections of this research work discuss introduction and preliminaries. In the remaining sections, the authors describe the multi soft set theory and its applications in rule base classification. Lastly, the paper finishes with a new algorithm, which the research scholars implemented as software using python programming. The suggested work experts can use in data mining industry. It has massive use in the fields of business, agriculture, health, education and many more.
Keywords: Soft Computing, Soft set, Multi soft set, Decision tree induction, Information gain, classification, Rule-based classification.