The patent classification problem has a very large scale dataset. Traditional classifiers cannot efficiently solve the problem. In this work, we introduce an improved parallel Min-Max Modular Support Vector Machine (M3-SVM) to solve the problem. Both theoretical analysis and experimental results show that M3-SVM has much less training time than standard SVMlight. The experimental results also show that M3-SVM can achieve higher F1 measure than SVMlight while predicting. Since the original M3-SVM costs too much time while predicting, in this work, we also introduce two pipelined parallel classifier selection algorithms to speed up the prediction process. Results on the patent classification experiments show that these two algorithms are pretty effective and scalable.
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Ye, ZF., Lu, BL., Hui, C. (2008). Patent Classification Using Parallel Min-Max Modular Support Vector Machine. In: Mahr, B., Huanye, S. (eds) Autonomous Systems – Self-Organization, Management, and Control. Springer, Dordrecht. https://doi.org/10.1007/978-1-4020-8889-6_17
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DOI: https://doi.org/10.1007/978-1-4020-8889-6_17
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