Classification of multiclass imbalanced data using cost-sensitive decision tree c5.0

Authors

M Febriantono
Sholeh Pramono
- RahmadwatiFollow
Golshah Naghdy, University of WollongongFollow

RIS ID

142062

Publication Details

M. Febriantono, S. Pramono, Rahmadwati & G. Naghdy, "Classification of multiclass imbalanced data using cost-sensitive decision tree c5.0," IAES International Journal of Artificial Intelligence, vol. 9, (1) pp. 65-72, 2020.

Abstract

© 2020, Institute of Advanced Engineering and Science. All rights reserved. The multiclass imbalanced data problems in data mining were interesting cases to study currently. The problems had an influence on the classification process in machine learning processes. Some cases showed that minority class in the dataset had an important information value compared to the majority class. When minority class was misclassification, it would affect the accuracy value and classifier performance. In this research, cost sensitive decision tree C5.0 was used to solve multiclass imbalanced data problems. The first stage, making the decision tree model uses the C5.0 algorithm then the cost sensitive learning uses the metacost method to obtain the minimum cost model. The results of testing the C5.0 algorithm had better performance than C4.5 and ID3 algorithms. The percentage of algorithm performance from C5.0, C4.5 and ID3 were 40.91%, 40, 24% and 19.23%.