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Optimization Model of K-Means Clustering Using Artificial Neural Networks to Handle Class Imbalance Problem
| Content Provider | Scilit |
|---|---|
| Author | Hartono Tulus Sitompul, O. S. Nababan, E. B. |
| Copyright Year | 2018 |
| Description | Journal: Iop Conference Series: Materials Science and Engineering 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. |
| Related Links | http://iopscience.iop.org/article/10.1088/1757-899X/288/1/012075/pdf |
| ISSN | 17578981 |
| e-ISSN | 1757899X |
| DOI | 10.1088/1757-899x/288/1/012075 |
| Journal | Iop Conference Series: Materials Science and Engineering |
| Issue Number | 1 |
| Volume Number | 288 |
| Language | English |
| Publisher | IOP Publishing |
| Publisher Date | 2018-01-25 |
| Access Restriction | Open |
| Subject Keyword | Journal: Iop Conference Series: Materials Science and Engineering Cybernetical Science Artificial Neural Networks Decision Making Class Imbalance K Means Imbalance Problem Class Means Clustering |
| Content Type | Text |
| Resource Type | Article |
