Imbalanced Learning: Foundations, Algorithms, and Applications by Haibo He & Yunqian Ma

Author:Haibo He & Yunqian Ma
Language: eng
Format: azw3
ISBN: 9781118074626
Publisher: Wiley
Published: 2013-06-07T04:00:00+00:00

Following the aforementioned methods of assigning membership values for positive and negative training data points, several FSVM-CIL settings have been defined in [40]. These methods have been validated on 10 real-world imbalanced datasets representing a variety of domains, complexities, and imbalanced ratios, which are highly likely to contain noisy examples and outliers. FSVM-CIL settings have resulted in better classification results on these datasets than the existing class imbalance learning methods applied for standard SVMs, namely random oversampling, random undersampling, SMOTE, DEC, and zSVM methods. Batuwita and Palade [40] pointed out that better performance of FSVM-CIL method is due to its capability to handle outliers and noise in these datasets in addition to the class imbalance problem.

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