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All Journal Bulletin of Electrical Engineering and Informatics
Asmaa Hekal Omar
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.
Even-odd crossover: a new crossover operator for improving the accuracy of students’ performance prediction Somia A. Shams; Asmaa Hekal Omar; Abeer S. Desuky; Mohammad T. Abou-Kreisha; Gaber A. Elsharawy
Bulletin of Electrical Engineering and Informatics Vol 11, No 4: August 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v11i4.3841

Abstract

Prediction using machine learning has evolved due to its impact on providing valuable and intuitive feedback. It has covered a wide range of areas for predicting student’ performance. Instructors can track student’s dropout in a particular course at an early stage and try to improve students’ performance. The problem of students’ future performance prediction using advanced statistics and machine learning is a hard problem due to the imbalanced nature of the student data where the number of students who passed the exam is generally much higher than the number of students who failed the exam. This paper proposes a new type of crossover operator called Even-Odd crossover to generate new instances into the minority class to handle the imbalanced data problem. The experiments are implemented using three machine learning (ML) algorithms: random forest (RF), support vector machines (SVM), and K-Nearest-Neighbor (KNN) to ensure the efficiency of the proposed technique. The performance of the classifiers is evaluated using several performance measures. The efficient ability of the proposed method on solving the imbalance problem is proved by performing the experiments on 22 real-world datasets from different fields and four students’ datasets. The proposed Even-Odd crossover shows superior performance compared to state-of-the-art resampling techniques.
Co-Authors Abeer S. Desuky Gaber A. Elsharawy Mohammad T. Abou-Kreisha Naglaa M. Mostafa Somia A. Shams