2021/06/02 by Maliheh Roknizadeh, Roknizadeh, Maliheh, Hossein Monshizadeh Naeen +1
Computer Science · Engineering · Health Professions · #Artificial Intelligence in Healthcare #Electricity Theft Detection Techniques #FOS: Computer and information sciences #Imbalanced Data Classification Techniques #Machine Learning (cs.LG) #Machine Learning and Data Classification #Neural and Evolutionary Computing (cs.NE) #cs.LG #cs.NE
paper · pdf · doi:10.48550/arxiv.2106.01176
11 pages, 4 Tables, 7 Figures Accepted in Twelfth International Conference on Information Technology, Computer and Telecommunications
arxiv created 2021/06/02 · openalex publication_date 2021/06/02 · arxiv updated 2021/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
One of the most significant current discussions in the field of data mining is classifying imbalanced data. In recent years, several ways are proposed such as algorithm level (internal) approaches, data level (external) techniques, and cost-sensitive methods. Although extensive research has been carried out on imbalanced data classification, however, several unsolved challenges remain such as no attention to the importance of samples to balance, determine the appropriate number of classifiers, and no optimization of classifiers in the combination of classifiers. The purpose of this paper is to improve the efficiency of the ensemble method in the sampling of training data sets, especially in the minority class, and to determine better basic classifiers for combining classifiers than existing methods. We proposed a hybrid ensemble algorithm based on Genetic Programming (GP) for two classes of imbalanced data classification. In this study uses historical data from UCI Machine Learning Repository to assess minority classes in imbalanced datasets. The performance of our proposed algorithm is evaluated by Rapid-miner studio v.7.5. Experimental results show the performance of the proposed method on the specified data sets in the size of the training set shows 40% and 50% better accuracy than other dimensions of the minority class prediction.