An Effective Recursive Technique for Multi-Class Classification and Regression for Imbalanced Data

In machine learning, classification and regression are two of the most noteworthy key topics since they occur extensively in numerous real-world applications. However, real life data is hardly ever found balanced, rather skewed data is the common occurrence. This poses some serious challenges to the...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.127615-127630
Main Authors: Alam, Tahira, Ahmed, Chowdhury Farhan, Zahin, Sabit Anwar, Khan, Muhammad Asif Hossain, Islam, Maliha Tashfia
Format: Article
Language:English
Subjects:
Algorithms
Bagging
Boosting
Classification
Classification algorithms
imbalance problem
Machine learning
multi-class classification
Partitioning algorithms
Performance enhancement
Recursive methods
regression
Regression analysis
Robustness (mathematics)
Sampling methods
Training
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Summary:In machine learning, classification and regression are two of the most noteworthy key topics since they occur extensively in numerous real-world applications. However, real life data is hardly ever found balanced, rather skewed data is the common occurrence. This poses some serious challenges to the standard techniques of classification and regression. The performance and effectiveness of these techniques are substantially affected by overfitting, creating a bias towards the majority class. In recent years, quite a few number of methods have been introduced for classification of imbalanced data. But most of them are designed for binary classes and it is difficult or inefficient to extend them for multiple classes. Moreover, data imbalance problem occurs frequently in regression analysis too, with only a handful of algorithms robust enough to tackle to this problem. In this paper, we propose an effective recursive method for multi-class classification with imbalanced data. Our proposed algorithm partitions and balances the data, and is applied recursively coupled with ensemble techniques. Furthermore, we also extend our proposed method to solve the data imbalance problem in regression analysis. Experimental results demonstrate that the proposed recursive technique is effective and improves the performance when compared to existing methods for classification and regression with imbalanced distribution.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2939755