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CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification by Rayhan, Farshid; Ahmed, Sajid; Mahbub, Asif; Jani, Md. Rafsan; Shatabda, Swakkhar; Farid, Dewan Md. is a scholarly article available to read on EtoBox.

What is CUSBoost: Cluster-based Under-sampling with Boosting for Imbalanced Classification about?

Class imbalance classification is a challenging research problem in data mining and machine learning, as most of the real-life datasets are often imbalanced in nature. Existing learning algorithms maximise the classification accuracy by correctly classifying the majority class, but misclassify the minority class. However, the minority class instances are representing the concept with greater interest than the majority class instances in real-life applications. Recently, several techniques based on sampling methods (under-sampling of the majority class and over-sampling the minority class), cost-sensitive learning methods, and ensemble learning have been used in the literature for classifying imbalanced datasets. In this paper, we introduce a new clustering-based under-sampling approach with boosting (AdaBoost) algorithm, called CUSBoost, for effective imbalanced classification. The proposed algorithm provides an alternative to RUSBoost (random under-sampling with AdaBoost) and SMOTEBoost (synthetic minority over-sampling with AdaBoost) algorithms. We evaluated the performance of CUSBoost algorithm with the state-of-the-art methods based on ensemble learning like AdaBoost, RUSBoost,

Author

Rayhan, Farshid; Ahmed, Sajid; Mahbub, Asif; Jani, Md. Rafsan; Shatabda, Swakkhar; Farid, Dewan Md.

Published

2017

Language

EN