MULTI-CLASS DATA CLASSIFICATION FOR IMBALANCED DATA SET USING COMBINED SAMPLING APPROACHES

Two important challenges in machine learning are the imbalanced class problem and multi-class classification, because several real-world applications have imbalanced class distribution and involve the classification of data into classes. The primary problem of classification in imbalanced data sets concerns measure of performance. The performance of standard learning algorithm tends to be biased towards the majority class and ignore the minority class. This paper presents a new approach (KSAMPLING), which is a combination of k-means clustering and sampling methods. K-means algorithm is used for spitting the dataset into two clusters. After that, we combine two types of sampling technique, over-sampling and under sampling, to re-balance the class distribution. We have conducted experiments on five highly imbalanced datasets from the UCI. Decision trees are used to classify the class of data. The experimental results showed that the prediction performance of KSAMPLING is better than the state-of-the-art methods in the AUC results and F-measure are also improved.