RDPVR: Random Data Partitioning with Voting Rule for Machine Learning from Class-Imbalanced Datasets

Electronics

Ghada Awad Altarawneh¹  Mansoor Alghamdi²  Malek Alrashidi²  Samer Subhi Abed³  Ahmad B. Hassanat³  Ahmad S. Tarawneh⁴ 

[1] Department of Accounting, Mutah University, Mutah, Karak 6171, Jordan;Department of Computer Science, Applied College, University of Tabuk, Tabuk 71491, Saudi Arabia;Faculty of Information Technology, Mutah University, Mutah, Karak 6171, Jordan;Faculty of informatics, Eotvos Lorand University, 1117 Budapest, Hungary;

关键词: classification;    data mining;    KNN;    CART;    SVM;    SMOTE;   

DOI  :  10.3390/electronics11020228

来源: DOAJ

【 摘 要 】

Since most classifiers are biased toward the dominant class, class imbalance is a challenging problem in machine learning. The most popular approaches to solving this problem include oversampling minority examples and undersampling majority examples. Oversampling may increase the probability of overfitting, whereas undersampling eliminates examples that may be crucial to the learning process. We present a linear time resampling method based on random data partitioning and a majority voting rule to address both concerns, where an imbalanced dataset is partitioned into a number of small subdatasets, each of which must be class balanced. After that, a specific classifier is trained for each subdataset, and the final classification result is established by applying the majority voting rule to the results of all of the trained models. We compared the performance of the proposed method to some of the most well-known oversampling and undersampling methods, employing a range of classifiers, on 33 benchmark machine learning class-imbalanced datasets. The classification results produced by the classifiers employed on the generated data by the proposed method were comparable to most of the resampling methods tested, with the exception of SMOTEFUNA, which is an oversampling method that increases the probability of overfitting. The proposed method produced results that were comparable to the Easy Ensemble (EE) undersampling method. As a result, for solving the challenge of machine learning from class-imbalanced datasets, we advocate using either EE or our method.

【 授权许可】

Unknown