کنفرانس مهندسی برق ایران

صفحه اصلی / سی و دومین کنفرانس بین المللی مهندسی برق

A novel clustering-based over-sampling technique for imbalanced data sets

نویسندگان :

Behzad Mirzaei¹ Hossein Nezamabadi-pour² Javad Mahmoodi³

1- دانشگاه شهید باهنر کرمان 2- دانشگاه شهید باهنر کرمان 3- دانشگاه شهید باهنر کرمان

کلمات کلیدی :

Imbalanced data،Clustering،K-means algorithm،Over-sampling،Preprocessing methods

چکیده :

One of the most challenging problems in machine learning is the classification of imbalanced data. This problem arises when the samples of data are distributed unevenly among the classes, such that compared to one class (the majority or negative class), the other class (the minority or positive class) has far fewer samples. The classical classifiers are inappropriate to classify data sets of this nature. To address these classifiers' shortcoming in class imbalance situations, we present a novel clustering-based over-sampling technique in this paper. First, the k-means clustering algorithm is used to cluster the minority class samples. Then, sparse clusters including fewer samples are chosen. Finally, we use the nearest neighbor of each cluster center to create synthetic samples for the minority class. Also, to select clusters based on probabilities, we apply the roulette wheel selection operator during over-sampling. The C4.5 decision tree classifier is utilized in our experiments, and the F-measure criterion is considered to evaluate methods. According to the results, our method outperforms six other methods over fifteen imbalanced data sets.