KSMOTEEN: A Cluster Based Hybrid Sampling Model for Imbalance Class Data

Classification accuracy for imbalance class data is a primary issue in machine learning. Most classification algorithms result in insignificant accuracy when used over class imbalance data. Class imbalance data exist in many sensitive domains such as medicine, finance, etc., where infrequent events such as rare disease diagnoses and fraud transactions are required to be identified. In these domains, correct classification is essential. The paper presents a hybrid sampling model called KSMOTEEN to address class imbalance data. The model uses a clustering approach, the K-means clustering algorithm, and combines the SMOTEEN technique. The experimental result shows, the KSMOTEEN outperforms some existing sampling methods, thus improving the performance of classifiers for class imbalance data.