Abstract
A short text classification method based on combination of KNN and hierarchical SVM is proposed. First, the KNN algorithm is improved to get the K nearest neighbor class labels quickly, so as to effectively filter the candidate classes of documents. And then classify them from top to bottom using a multi-class sparse hierarchical SVM classifier. By this way, the document can be classified efficiently.
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Acknowledgments
This work was funded by the National Natural Science Foundation of China (61373134). It was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Jiangsu Key Laboratory of Meteorological Observation and Information Processing (KDXS1105) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET). We declare that we do not have any conflicts of interest to this work.
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Yin, C., Shi, L., Wang, J. (2017). Short Text Classification Technology Based on KNN+Hierarchy SVM. In: Park, J., Chen, SC., Raymond Choo, KK. (eds) Advanced Multimedia and Ubiquitous Engineering. FutureTech MUE 2017 2017. Lecture Notes in Electrical Engineering, vol 448. Springer, Singapore. https://doi.org/10.1007/978-981-10-5041-1_100
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DOI: https://doi.org/10.1007/978-981-10-5041-1_100
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