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Article Details

Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.

Novel Oversampling Algorithm for Handling Imbalanced Data Classification

Author 1: Anjali S. More
Author 2: Dipti P. Rana

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Digital Object Identifier (DOI) : 10.14569/IJACSA.2022.0130856

Article Published in International Journal of Advanced Computer Science and Applications(IJACSA), Volume 13 Issue 8, 2022.

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Abstract: In the current age, the attention of researchers is immersed by numerous imbalanced data applications. These application areas are intrusion detection in security, fraud recognition in finance, medical applications dealing with disease diagnosis pilfering in electricity, and many more. Imbalanced data applications are categorized into two types: binary and multiclass data imbalance. Unequal data distribution among data diverts classification performance metrics towards the majority data instance class and ignores the minority data, instance class. Data imbalance leads to an increase in the classification error rate. Random Forest Classification (RFC) is best suitable technique to deal with imbalanced datasets. This paper proposes the novel oversampling rate calculation algorithm as Improvised Dynamic Binary-Multiclass Imbalanced Oversampling Rate (IDBMORate). Experimentation analysis of the proposed novel approach IDBMORate on Page-block (Binary) dataset shows that instances of positive class is increased from 559 to 1118 whereas negative instance class remains same as 4913. In case of referred multiclass dataset (Ecoli), IDBMORate produces the consistent result as minority classes (om, omL, imS, imL) instances are oversampled majority class instances remains unchanged. IDBMORate algorithm reduces the ignorance of minority class and oversamples its data without disturbing the size of the majority instance class. Thus, it reduces the overall computation cost and leads towards the improvisation of classification performance.

Keywords: Binary imbalance; multiclass imbalance; oversampling; random forest classification; classification

Anjali S. More and Dipti P. Rana, “Novel Oversampling Algorithm for Handling Imbalanced Data Classification” International Journal of Advanced Computer Science and Applications(IJACSA), 13(8), 2022. http://dx.doi.org/10.14569/IJACSA.2022.0130856

@article{More2022,
title = {Novel Oversampling Algorithm for Handling Imbalanced Data Classification},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2022.0130856},
url = {http://dx.doi.org/10.14569/IJACSA.2022.0130856},
year = {2022},
publisher = {The Science and Information Organization},
volume = {13},
number = {8},
author = {Anjali S. More and Dipti P. Rana}
}


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