Comparative analysis of resampling techniques on Machine Learning algorithm

Authors

  • Tri Suci Amelia Universitas Labuhanbatu
  • Mila Nirmala Sari Hasibuan Universitas Labuhanbatu, Indonesia
  • Rahmadani Pane Universitas Labuhanbatu, Indonesia

DOI:

10.33395/sinkron.v7i2.11427

Abstract

Generally, classification algorithms in the field of data science assume that the classes of training data are equally distributed. However, datasets on real problems often have an unbalanced class distribution. Unbalanced dataset classes make up the majority class and the minority class. In general, minority classes are more attractive and more important to identify. In this case, the correct classification for the minority class sample is more valuable than the majority class. The unbalanced class distribution causes the classification algorithm to have difficulty in classifying minority class samples correctly. If the performance of the algorithm model is good for the majority class sample but bad for the minority class then this imbalance problem is a crucial thing to be addressed. Many solutions are offered for this problem, namely by oversampling techniques in the minority class and/or undersampling techniques in the majority class. In this study, the authors tried various sampling techniques and tested them on various machine learning classification algorithms to find out the combination of resampling techniques and algorithms that have high recall in classifying minority class samples and still considering the majority class classification.

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How to Cite

Amelia, T. S., Hasibuan, M. N. S. ., & Pane, R. . (2022). Comparative analysis of resampling techniques on Machine Learning algorithm. Sinkron : Jurnal Dan Penelitian Teknik Informatika, 6(2), 628-634. https://doi.org/10.33395/sinkron.v7i2.11427

Issue

Vol. 6 No. 2 (2022): Articles Research Volume 6 Issue 2, April 2022

Section

Articles

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Copyright (c) 2022 Tri Suci Amelia, Mila Nirmala Sari Hasibuan, Rahmadani Pane

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

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