Affiliation:
1. Big Data & Intelligent System Research Group, Dalian Jiaotong University, Dalian, China
Abstract
In recent years, imbalanced data learning has attracted a lot of attention from academia and industry as a new challenge. In order to solve the problems such as imbalances between and within classes, this paper proposes an adaptive boundary weighted synthetic minority oversampling algorithm (ABWSMO) for unbalanced datasets. ABWSMO calculates the sample space clustering density based on the distribution of the underlying data and the K-Means clustering algorithm, incorporates local weighting strategies and global weighting strategies to improve the SMOTE algorithm to generate data mechanisms that enhance the learning of important samples at the boundary of unbalanced data sets and avoid the traditional oversampling algorithm generate unnecessary noise. The effectiveness of this sampling algorithm in improving data imbalance is verified by experimentally comparing five traditional oversampling algorithms on 16 unbalanced ratio datasets and 3 classifiers in the UCI database.
Subject
Artificial Intelligence,General Engineering,Statistics and Probability
Reference27 articles.
1. An Oversampling Algorithm Based on Hierarchical Clustering and Improved SMOTE;AGNES-SMOTE;Scientific Programming,2020
2. Data sampling methods to deal with the big data multi-class imbalance problem;Alejo;Applied Sciences,2020
3. Learning from imbalanced data sets with weighted crossentropy function;Aurelio;Neural Processing Letters,2019
4. Błaszczyński J. and Stefanowski J. , Local data characteristics in learning classifiers from imbalanced data, Advances in Data Analysis with Computational Intelligence Methods. Springer, Cham (2018), 51–85.
5. SMOTE: Synthetic minority over-sampling technique;Chawla;Journal of Artificial Intelligence Research