Entropy-Based Fuzzy Weighted Logistic Regression for Classifying Imbalanced Data

Ajiwasesa Harumeka*, Santi Wulan Purnami, Santi Puteri Rahayu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)


Logistic regression is a popular classification method that has disadvantages when it is applied to large data. Truncated Regularized Iteratively Reweighted Least Square (TR-IRLS) is a method that overcomes this problem. This method is similar to Support Vector Machine (SVM) because both of them have similar loss functions and parameters that can adjust the bias and variance. Both methods were designed with the assumption of balanced data, so that they are not suitable to be applied on imbalanced data. Both methods were developed to overcome problem on imbalanced data. TR-IRLS was developed into Rare Event Weighted Logistic Regression (RE-WLR) and SVM was developed into Fuzzy Support Vector Machine (FSVM). Both RE-WLR and FSVM use weights based on class differences, so that RE-WLR had better performance than TR-IRLS on imbalanced data whereas FSVM was better than SVM. Then, Entropy-based Fuzzy Support Vector Machine (EFSVM) was developed by obtaining weighting values not only based on class differences, but also based on entropy. EFSVM further enhanced minority class interest in imbalanced data than SVM and even FSVM. Therefore, Entropy-based Fuzzy Weighted Logistic Regression (EFWLR) is proposed by adopting the success of Entropy-based Fuzzy Membership (EF) as weight on SVM. This study applied EF as weight on Weighted Logistic Regression for binary classification. Experiments on 20 simulation data and 5 benchmark data with various rarity schemes validated that the EFWLR outperformed TR-IRLS and RE-WLR based on AUC. EFWLR had more efficient AUC than RE-WLR on imbalanced data.

Original languageEnglish
Title of host publicationSoft Computing in Data Science - 6th International Conference, SCDS 2021, Proceedings
EditorsAzlinah Mohamed, Bee Wah Yap, Jasni Mohamad Zain, Michael W. Berry
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages16
ISBN (Print)9789811673337
Publication statusPublished - 2021
Event6th International Conference on Soft Computing in Data Science, SCDS 2021 - Virtual, Online
Duration: 2 Nov 20213 Nov 2021

Publication series

NameCommunications in Computer and Information Science
Volume1489 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference6th International Conference on Soft Computing in Data Science, SCDS 2021
CityVirtual, Online


  • Binary classification
  • Entropy-based Fuzzy
  • Imbalanced Data
  • Weighted Logistic Regression


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