Adaptive Ensemble Undersampling-Boost: A novel learning framework for imbalanced data

Wei Lü; Zhe Li; Jinghui Chu

doi:10.1016/j.jss.2017.07.006

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Adaptive Ensemble Undersampling-Boost: A novel learning framework for imbalanced data

Wei Lü Zhe Li Jinghui Chu

Year: 2017 Journal: Journal of Systems and Software Vol: 132 Pages: 272-282 Publisher: Elsevier BV

DOI: 10.1016/j.jss.2017.07.006

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Keywords:

Undersampling Computer science Machine learning Boosting (machine learning) Ensemble learning Artificial intelligence AdaBoost Decision boundary Resampling Thresholding Oversampling Binary classification Class (philosophy) Data mining Pattern recognition (psychology) Support vector machine

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Imbalanced Data Classification Techniques

Physical Sciences → Computer Science → Artificial Intelligence

Electricity Theft Detection Techniques

Physical Sciences → Engineering → Electrical and Electronic Engineering

Financial Distress and Bankruptcy Prediction

Social Sciences → Business, Management and Accounting → Accounting

Adaptive Ensemble Undersampling-Boost: A novel learning framework for imbalanced data

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