Anzeige
Erschienen in:
2014 | OriginalPaper | Buchkapitel
Stochastic Sensitivity Oversampling Technique for Imbalanced Data
verfasst von : Tongwen Rong, Huachang Gong, Wing W. Y. Ng
Erschienen in: Machine Learning and Cybernetics
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
Data level technique is proved to be effective in imbalance learning. The SMOTE is a famous oversampling technique generating synthetic minority samples by linear interpolation between adjacent minorities. However, it becomes inefficiency for datasets with sparse distributions. In this paper, we propose the Stochastic Sensitivity Oversampling (SSO) which generates synthetic samples following Gaussian distributions in the Q-union of minority samples. The Q-union is the union of Q-neighborhoods (hypercubes centered at minority samples) and such that new samples are synthesized around minority samples. Experimental results show that the proposed algorithm performs well on most of datasets, especially those with a sparse distribution.