Abstract
With the recent development of weblogs and social networks, many supplier industries share their data on different websites and weblogs. Even the Small-to-Medium sized enterprises (SMEs) in the manufacturing sector (as well as non-manufacturing sector) are rapidly strengthening their web presence in order to improve their visibility, customer reachability and remain competitive in the global market. Our study aims to classify data into various groups so that users can identify the most appropriate content based on their choice at any given time. To classify and characterize manufacturing suppliers in supply chain through their capability narratives and textual portfolios obtained from websites of such suppliers online source portals for testing and Naïve Bayes and support vector machine (SVM) Classification method at term-level for classification has been used. The performance of the proposed classifier was tested experimentally based on the standard metrics such as precision, recall, and F-measure.
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References
Martens, D., Provost, F.: Explaining data-driven document classifications (2013)
Liu, T.Y.: Learning to rank for information retrieval. Found. Trends® Inf. Retrieval 3(3), 225–331 (2009)
Korde, V., Mahender, C.N.: Text classification and classifiers: a survey. Int. J. Artif. Intell. Appl. 3(2), 85 (2012)
Qi, X., Davison, B.D.: Web page classification: features and algorithms. ACM Comput. Surv. (CSUR) 41(2), 12 (2009)
Sanchez-Pi, N., Martí, L., Garcia, A.C.B.: Text classification techniques in oil industry applications. In: International Joint Conference SOCO 2013-CISIS 2013-ICEUTE 2013, pp. 211–220. Springer International Publishing (2014)
Hallikas, J., Puumalainen, K., Vesterinen, T., Virolainen, V.M.: Risk-based classification of supplier relationships. J. Purchasing Supply Manage. 11(2), 72–82 (2005)
Han, J., Pei, J., Kamber, M.: Data mining: concepts and techniques. Elsevier (2011)
Manupati, V.K., Anand, R., Thakkar, J.J., Benyoucef, L., Garcia, F.P., Tiwari, M.K.: Adaptive production control system for a flexible-manufacturing cell using support vector machine-based approach. Int. J. Adv. Manufact. Technol., 1–20 (2012)
Acknowledgment
This work has been supported by Department of Science and Technology, Science & Engineering Research Board (SERB), Statutory Body Established through an Act of Parliament: SERB Act 2008, Government of India with Sanction Order No ECR/2016/001808.
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Akhtar, M.D., Manupati, V.K., Varela, M.L.R., Putnik, G.D., Madureira, A.M., Abraham, A. (2018). Manufacturing Services Classification in a Decentralized Supply Chain Using Text Mining. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Hybrid Intelligent Systems. HIS 2017. Advances in Intelligent Systems and Computing, vol 734. Springer, Cham. https://doi.org/10.1007/978-3-319-76351-4_19
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DOI: https://doi.org/10.1007/978-3-319-76351-4_19
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