| dc.description.abstract | The class imbalance problem is an important issue in data mining. This problem occurs when the number of samples that represent one class is much less than the ones of other classes. The classification model built by class imbalance datasets is likely to misclassify most samples in the minority class into the majority class because of maximizing the accuracy rate. It’s presences in many real-world applications, such as fault diagnosis, medical diagnosis or face recognition.
One of the most popular types of solutions is to consider data sampling. For example, Under-sampling the majority class or over-sampling the minority class to balance the imbalance datasets. Under-sampling balance class distribution through the elimination of majority class samples, but it may discard useful data. On the contrary, over-sampling replicates minority class samples, but it can increase the likelihood of occurring overfitting.
Therefore, we propose several resampling methods based on the k-means clustering technique. In order to decrease the probability of uneven resampling, we select representative samples to replace majority class samples in the training dataset.
Our experiments are based on using 44 small class imbalance datasets and two large scale datasets to build five types of classification models, which are C4.5, SVM, MLP, k-NN (k=5) and Naïve Bayes. In addition, the classifier ensemble algorithm is also employed. The research tries to compare the AUC result between different resampling techniques, different models and the number of clusters. Besides, we also divide imbalance ratio into three intervals. We try to find the best configuration of our experiments and compete with other literature methods. The experimental results show that combining the MLP classifier with the clustering based under-sampling method by the nearest neighbors of the cluster centers performs the best in terms of AUC over small and large scale datasets. | en_US |