Cost-Sensitive Boosting Algorithm for Classifying Underdeveloped Regions in Indonesia

Authors

  • Bayu Suseno IPB University
  • Bagus Sartono Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University
  • Khairil Anwar Notodiputro Department of Statistics, Faculty of Mathematics and Natural Sciences, IPB University

DOI:

https://doi.org/10.34123/icdsos.v2023i1.373

Keywords:

Cost-Sensitive Boosting, Imbalanced Data, Underdeveloped, AdaC2, Machine Learning

Abstract

Imbalanced classes are indicated by having more instances of some classes than others. The cost-sensitive boosting algorithm is a modification of the AdaBoost algorithm, which aims to solve the problem of imbalanced classes. In this study, we evaluate the cost-sensitive Boosting algorithm AdaC2 using Indonesia's underdeveloped region's data. This study confirms that the cost-sensitive boosting algorithm (AdaC2) performs better in classifying the instances in the minority classes than standard classifiers algorithms.

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Published

2023-12-29

How to Cite

Suseno, B., Sartono, B., & Notodiputro, K. A. (2023). Cost-Sensitive Boosting Algorithm for Classifying Underdeveloped Regions in Indonesia. Proceedings of The International Conference on Data Science and Official Statistics, 2023(1), 296–308. https://doi.org/10.34123/icdsos.v2023i1.373

Issue

Vol. 2023 No. 1 (2023): Proceedings of 2023 International Conference on Data Science and Official Statistics (ICDSOS)

Section

Data Science