Abstract
Recently, the use of Receiver Operating Characteristic (ROC) Curve and the area under the ROC Curve (AUC) has been receiving much attention as a measure of the performance of machine learning algorithms. In this paper, we propose a SVM classifier fusion model using genetic fuzzy system. Genetic algorithms are applied to tune the optimal fuzzy membership functions. The performance of SVM classifiers are evaluated by their AUCs. Our experiments show that AUC-based genetic fuzzy SVM fusion model produces not only better AUC but also better accuracy than individual SVM classifiers.
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Chen, X., Zhao, Y., Zhang, YQ., Harrison, R. (2007). Combining SVM Classifiers Using Genetic Fuzzy Systems Based on AUC for Gene Expression Data Analysis. In: Măndoiu, I., Zelikovsky, A. (eds) Bioinformatics Research and Applications. ISBRA 2007. Lecture Notes in Computer Science(), vol 4463. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72031-7_45
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DOI: https://doi.org/10.1007/978-3-540-72031-7_45
Publisher Name: Springer, Berlin, Heidelberg
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