Minority oversampling based on the attraction-repulsion Weber problem
Concurrency and Computation: Practice and Experience, ISSN: 1532-0634, Vol: 32, Issue: 18
2020
- 2Citations
- 4Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Conference Paper Description
Learning on imbalanced datasets, where one class is underrepresented, is problematic and important at the same time. On the one hand, a limited number of positive examples restricts the generalization ability of classifiers. On the other hand, often, the class of interest is such exactly because it is rare. The Synthetic Minority Oversampling TEchnique (SMOTE) is a preprocessing method that creates new synthetic examples by interpolating between neighboring instances. In this work, an enhancement to SMOTE is proposed, which characterizes synthetic instances as solutions of attraction-repulsion problems among the neighboring data points. Experimental evaluation shows an improvement in the positive predictive power of classification.
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