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Naglaa M. Mostafa
Al-Azhar University
Electrical & Electronics Engineering

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Boosting with crossover for improving imbalanced medical datasets classification Abeer S. Desuky; Asmaa Hekal Omar; Naglaa M. Mostafa
Bulletin of Electrical Engineering and Informatics Vol 10, No 5: October 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i5.3121

Abstract

Due to the common use of electronic health databases in many healthcare services, healthcare data are available for researchers in the classification field to make diseases’ diagnosis more efficient. However, healthcare-medical data classification is most challenging because it is often imbalanced data. Most proposed algorithms are susceptible to classify the samples into the majority class, resulting in the insufficient prediction of the minority class. In this paper, a novel preprocessing method is proposed, using boosting and crossover to optimize the ratio of the two classes by progressively rebuilding the training dataset. This approach is shown to give better performance than other state-of-the-art ensemble methods, which is demonstrated by experiments on seven real-world medical datasets with different imbalance ratios and various distributions.
Co-Authors Abeer S. Desuky Asmaa Hekal Omar