Random and Synthetic Over-Sampling Approach to Resolve Data Imbalance in Classification

International Journal of artificial intelligence research

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Field Value
 
Title Random and Synthetic Over-Sampling Approach to Resolve Data Imbalance in Classification
 
Creator Hayaty, Mardhiya
Muthmainah, Siti
Ghufran, Syed Muhammad
 
Subject

 
Description High accuracy value is one of the parameters of the success of classification in predicting classes. The higher the value, the more correct the class prediction.  One way to improve accuracy is dataset has a balanced class composition. It is complicated to ensure the dataset has a stable class, especially in rare cases. This study used a blood donor dataset; the classification process predicts donors are feasible and not feasible; in this case, the reward ratio is quite high. This work aims to increase the number of minority class data randomly and synthetically so that the amount of data in both classes is balanced. The application of SOS and ROS succeeded in increasing the accuracy of inappropriate class recognition from 12% to 100% in the KNN algorithm. In contrast, the naïve Bayes algorithm did not experience an increase before and after the balancing process, which was 89%. 
 
Publisher STMIK Dharma Wacana
 
Contributor
 
Date 2021-01-05
 
Type info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
Peer-reviewed Article
 
Format application/pdf
 
Identifier http://ijair.id/index.php/ijair/article/view/152
10.29099/ijair.v4i2.152
 
Source International Journal of Artificial Intelligence Research; Vol 4, No 2 (2020): December; 86 - 94
2579-7298
10.29099/ijair.v4i2
 
Language eng
 
Relation http://ijair.id/index.php/ijair/article/view/152/pdf
 
Rights Copyright (c) 2021 International Journal of Artificial Intelligence Research
https://creativecommons.org/licenses/by-sa/4.0
 

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