Abstract
In this work, a hybrid model comprising Particle Swarm Optimization (PSO) and the Fuzzy Support Vector Machine (FSVM) for tackling imbalanced classification problems is proposed. A PSO algorithm, guided by the G-mean measure, is used to optimize the FSVM parameters in imbalanced classification problems. The hybrid PSO-FSVM model is evaluated using a mammogram mass classification problem. The experimental results are analyzed and compared with those from other methods. The outcomes positively demonstrate that the proposed PSO-FSVM model is able to achieve comparable, if not better, results for imbalanced data classification problems.
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Samma, H., Lim, C.P., Ngah, U.K. (2013). A Hybrid PSO-FSVM Model and Its Application to Imbalanced Classification of Mammograms. In: Selamat, A., Nguyen, N.T., Haron, H. (eds) Intelligent Information and Database Systems. ACIIDS 2013. Lecture Notes in Computer Science(), vol 7802. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36546-1_29
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DOI: https://doi.org/10.1007/978-3-642-36546-1_29
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