2022/10/22 by Shivaditya Shivganesh, Nitin Narayanan N, Shivganesh, Shivaditya +6
Business, Management and Accounting · Computer Science · Engineering · Mathematics · #Artificial intelligence #Class (philosophy) #Computer science #Data mining #Decision tree #Electricity Theft Detection Techniques #FOS: Computer and information sciences #Financial Distress and Bankruptcy Prediction #Imbalanced Data Classification Techniques #Information Theory (cs.IT) #Machine Learning (cs.LG) #Machine learning #Oversampling #Pattern recognition (psychology) #Relation (database) #Resampling #Sampling (signal processing) #Variety (cybernetics) #cs.IT #cs.LG #math.IT
paper · pdf · doi:10.48550/arxiv.2210.12446
published in arXiv (Cornell University) (Cornell University)
arxiv created 2022/10/22 · openalex publication_date 2022/10/22 · arxiv updated 2022/10/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study is about inducing classifiers using data that is imbalanced, with a minority class being under-represented in relation to the majority classes. The first section of this research focuses on the main characteristics of data that generate this problem. Following a study of previous, relevant research, a variety of artificial, imbalanced data sets influenced by important elements were created. These data sets were used to create decision trees and rule-based classifiers. The second section of this research looks into how to improve classifiers by pre-processing data with resampling approaches. The results of the following trials are compared to the performance of distinct pre-processing re-sampling methods: two variants of random over-sampling and focused under-sampling NCR. This paper further optimises class imbalance with a new method called Sparsity. The data is made more sparse from its class centers, hence making it more homogenous.