Abstract
With the rapid development of the Internet, online recruitment has gradually become mainstream. However, job seekers need to spend a lot of time to find a suitable job when there are a large variety of job information, which will seriously affect their efficiency, so it is necessary to carry out more detailed and efficient classification of the recruitment documents. Currently, common text classification algorithms include KNN (k-Nearest Neighbor), SVM (Support Vector Machine) and Naïve Bayes. Particularly, KNN algorithm is widely used in text classification for its simple implementation and accurate classification. But KNN algorithm has been criticized for its inefficiency in the face of large-scale recruitment. This paper improves the original KNN algorithm and proposes RS-KNN algorithm to achieve rapid refinement and classification of recruitment information. Experiments show that the improved algorithm has higher efficiency and less time consumption than the original algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Gai, K., Qiu, M.: Reinforcement learning-based content-centric services in mobile sensing. IEEE Netw. 32(4), 34–39 (2018)
Gai, K., Xu, K., Lu, Z., Qiu, M., Zhu, L.: Fusion of cognitive wireless networks and edge computing. IEEE Wirel. Commun. 26(3), 69–75 (2019)
Gai, K., Qiu, M., Zhao, H.: Energy-aware task assignment for mobile cyber-enabled applications in heterogeneous cloud computing. J. Parallel Distrib. Comput. 111, 126–135 (2018)
Yin, H., Gai, K., Wang, Z.: A classification algorithm based on ensemble feature selections for imbalanced-class dataset. In: 2016 IEEE 2nd International Conference on Big Data Security on Cloud, pp. 245–249. IEEE (2016)
Yin, H., Gai, K.: An empirical study on preprocessing high-dimensional class-imbalanced data for classification. In: 2015 IEEE 17th International Conference on High Performance Computing and Communications, 2015 IEEE 7th International Symposium on Cyberspace Safety and Security, and 2015 IEEE 12th International Conference on Embedded Software and Systems, pp. 1314–1319. IEEE (2015)
Wang, Y., Chaib-draa, B.: KNN-based Kalman filter: an efficient and non-stationary method for gaussian process regression. Knowl.-Based Syst. 114, 148–155 (2016)
Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2(Nov), 45–66 (2001)
Lai, S., Xu, L., Liu, K., Zhao, J.: Recurrent convolutional neural networks for text classification. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)
Yuan, X., Sun, M., Chen, Z., Gao, J., Li, P.: Semantic clustering-based deep hypergraph model for online reviews semantic classification in cyber-physical-social systems. IEEE Access 6, 17942–17951 (2018)
Yang, K., Cai, Y., Cai, Z., Xie, H., Wong, T., Chan, W.: Top k representative: a method to select representative samples based on k nearest neighbors. Int. J. Mach. Learn. Cybern. 10, 1–11 (2017)
Gu, S., Cheng, R., Jin, Y.: Feature selection for high-dimensional classification using a competitive swarm optimizer. Soft Comput. 22(3), 811–822 (2018)
Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5, 135–146 (2017)
Yang, H., Cui, H., Tang, H.: A text classification algorithm based on feature weighting. In: AIP Conference Proceedings, vol. 1864, p. 020026. AIP Publishing (2017)
Heydon, A., Najork, M.: Mercator: a scalable, extensible web crawler. World Wide Web 2(4), 219–229 (1999)
Goetz, B.: The lucene search engine: powerful, flexible, and free. JavaWorld (2000). http://www.javaworld.com/javaworld/jw-09-2000/jw-0915-lucene.html
Carpenter, B.: Lingpipe for 99.99% recall of gene mentions. In: Proceedings of the 2nd BioCreative Challenge Evaluation Workshop, vol. 23, pp. 307–309. BioCreative (2007)
Fienberg, S.: The use of chi-squared statistics for categorical data problems. J. Roy. Stat. Soc.: Ser. B(Methodol.) 41(1), 54–64 (1979)
Bennasar, M., Hicks, Y., Setchi, R.: Feature selection using joint mutual information maximisation. Expert Syst. Appl. 42(22), 8520–8532 (2015)
Wang, X., et al.: Research and implementation of a multi-label learning algorithm for Chinese text classification. In: 2017 3rd International Conference on Big Data Computing and Communications (BIGCOM), pp. 68–76. IEEE (2017)
Ma, Y., Li, Y., Wu, X., Zhang, X.: Chinese text classification review. In: 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 737–739. IEEE (2018)
Zhao, Y., Qian, Y., Li, C.: Improved KNN text classification algorithm with MapReduce implementation. In: 2017 4th International Conference on Systems and Informatics (ICSAI), pp. 1417–1422. IEEE (2017)
Acknowledgement
This work is supported by Postgraduate Education Innovation and Quality Improvement Project of Henan University, Henan University (SYL18020105).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Qin, W., Guo, W., Liu, X., Zhao, H. (2019). A Novel Scheme for Recruitment Text Categorization Based on KNN Algorithm. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2019. Lecture Notes in Computer Science(), vol 11910. Springer, Cham. https://doi.org/10.1007/978-3-030-34139-8_38
Download citation
DOI: https://doi.org/10.1007/978-3-030-34139-8_38
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-34138-1
Online ISBN: 978-3-030-34139-8
eBook Packages: Computer ScienceComputer Science (R0)