Optimization Model of K-Means Clustering Using Artificial Neural Networks to Handle Class Imbalance Problem

2nd Annual Applied Science and Engineering Conference

工业技术;自然科学

Hartono^1,2 ; Sitompul, O.S.^2 ; Tulus^3 ; Nababan, E.B.^2

Department of Computer Science, STMIK IBBI, Medan, Indonesia^1

Department of Computer Science, University of Sumatera Utara, Medan, Indonesia^2

Department of Mathematics, University of Sumatera Utara, Medan, Indonesia^3

关键词: Class imbalance;    Class imbalance problems;    Decision making process;    K;    means clustering;    Machine learning techniques;    Optimization modeling;    Prediction accuracy;    UCI machine learning repository;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/288/1/012075/pdf
DOI  :  10.1088/1757-899X/288/1/012075

来源: IOP

【 摘 要 】

Class imbalance is a situation where instances in one class much higher than instances in other classes. In clustering, this problem not only affects the accuracy of a prediction but also introduces bias in decision-making process. In this case, a machine learning technique will yield a good prediction accuracy from training data class with a large number of instances, but give a poor accuracy in classes with the small number of instances. In this research, we propose an approach for optimizing K-Means clustering in handling class imbalance problem. The approach uses the perceptron feed-forward neural network to determine coordinates of the centroid of a cluster in K-Means clustering processes. Data used in this research are datasets from the UCI Machine Learning Repository. From the experimental results obtained, the proposed approach could optimize the result of K-Means clustering in terms of minimizing class imbalance.