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.
Highlights
Data mining is not a single task; it consists of a set of different operations known as Data mining functionalities
To overcome the difficulties of rule-based classification using decision tree induction method, we propose an alternative method with the help of multi-soft set theory
Among them rule-based classification using decision tree induction method is widely popular in data mining industry
Summary
Data mining is not a single task; it consists of a set of different operations known as Data mining functionalities. Algorithms like - decision tree induction, Bayesian classification, Rule-based classification and classification by backpropagation are using for classification Another branch of computing is emerged and continuing its growth by simplifying the implementation complexity of different functionalities of data mining. This computing is known as soft computing. Rule-based classification using decision tree induction is the simplest method for predicting the class of an unknown entity. A day this technique is very popular, because it does not need a domain knowledge and parameter setting. To overcome the difficulties of rule-based classification using decision tree induction method, we propose an alternative method with the help of multi-soft set theory
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