A novel approach for solving skewed classification problem using cluster based ensemble method
| Autor: | Amit Kumar Tyagi, V. Krishna Reddy, Gillala Rekha |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Boosting (machine learning)
business.industry Computer science Machine learning computer.software_genre Ensemble learning Theoretical Computer Science Computational Mathematics Class imbalance ComputingMethodologies_PATTERNRECOGNITION Computational Theory and Mathematics Binary classification Artificial Intelligence Oversampling Artificial intelligence AdaBoost business computer Classifier (UML) Cluster based |
| Zdroj: | Mathematical Foundations of Computing. 3:1-9 |
| ISSN: | 2577-8838 |
| DOI: | 10.3934/mfc.2020001 |
| Popis: | In numerous real-world applications, the class imbalance problem is prevalent. When training samples of one class immensely outnumber samples of the other classes, the traditional machine learning algorithms show bias towards the majority class (a class with more number of samples) lead to significant losses of model performance. Several techniques have been proposed to handle the problem of class imbalance, including data sampling and boosting. In this paper, we present a cluster-based oversampling with boosting algorithm (Cluster+Boost) for learning from imbalanced data. We evaluate the performance of the proposed approach with state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost and SMOTEBoost. We conducted experiments on 22 data sets with various imbalance ratios. The experimental results are promising and provide an alternative approach for improving the performance of the classifier when learned on highly imbalanced data sets. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|
| Nepřihlášeným uživatelům se plný text nezobrazuje | K zobrazení výsledku je třeba se přihlásit. |