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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References
Zhang, H., Zhong, G.: Improving short text classification by learning vector representations of both words and hidden topics. Knowl. Based Syst. 102, 76–86 (2016)
Yin, C., Xi, J.: Maximum entropy model for mobile text classification in cloud computing using improved information gain algorithm. Multimedia Tools Appl., 1–17 (2016)
Altınel, B., Ganiz, M.C.: A new hybrid semi-supervised algorithm for text classification with class-based semantics. Knowl. Based Syst. 108, 50–64 (2016)
Yin, C., Xiang, J., et al.: Short text classification algorithm based on semi-supervised learning and SVM. Int. J. Multimedia Ubiquit. Eng. 10(12), 195–206 (2015)
Tang, X., Xu, A.: Multi-class classification using kernel density estimation on K-nearest neighbours. Electron. Lett. 52, 600–602 (2016)
Dong, C., Zhou, B., Hu, J.: A hierarchical SVM based multiclass classification by using similarity clustering. In: International Joint Conference on Neural Networks, pp. 1–6 (2015)
Tanveer, M., et al.: An efficient regularized K-nearest neighbor based weighted twin support vector regression. Knowl. Based Syst. 94, 70–87 (2016)
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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