Open AccessPerformance of the Hybrid Approach based on Rough Set Theory
Author(s) -
Betül Kan Kılınç,
Yonca Yazirli
Publication year - 2020
Publication title -
pakistan journal of statistics and operation research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.354
H-Index - 15
eISSN - 2220-5810
pISSN - 1816-2711
DOI - 10.18187/pjsor.v16i2.3069
Subject(s) - rough set , reduct , multinomial logistic regression , mathematics , support vector machine , data mining , set (abstract data type) , machine learning , data set , artificial intelligence , computer science , statistics , programming language
One of the essential problems in data mining is the removal of negligible variables from the data set. This paper proposes a hybrid approach that uses rough set theory based algorithms to reduct the attribute selected from the data set and utilize reducts to raise the classification success of three learning methods; multinomial logistic regression, support vector machines and random forest using 5-fold cross validation. The performance of the hybrid approach is measured by related statistics. The results show that the hybrid approach is effective as its improved accuracy by 6-12% for the three learning methods.