Abstract
In this study, we formalize a multifocal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. The multifocal learning problem is motivated by numerous real-world learning applications. For instance, for the same type of problems encountered in a customer service center, the problem descriptions from different customers can be quite different. Experienced customers usually give more precise and focused descriptions about the problem. In contrast, inexperienced customers usually provide diverse descriptions. In this case, the examples from the same class in the training data can be naturally in different focal groups. Therefore, it is necessary to identify those natural focal groups and exploit them for learning at different focuses. Along this line, the key development challenge is how to identify those focal groups in the training data. As a case study, we exploit multifocal learning for profiling customer problems. Also, we provide an empirical study about how the performance of multifocal learning is affected by the quality of focal groups. The results on real-world customer problem logs show that multifocal learning can significantly boost the performance of many existing classification algorithms, such as Support Vector Machines (SVMs), for classifying customer problems and there is strong correlation between the quality of focal groups and the learning performance.
- Bloehdorn, S. 2004. Text classification by boosting weak learners based on terms and concepts. In Proceedings of the 4th IEEE International Conference on Data Mining. 331--334. Google ScholarDigital Library
- Brown, L., Gans, N., Mandelbaum, A., Sakov, A., Shen, H., Zeltyn, S., and Zhao, L. 2005. Statistical analysis of a telephone call center: A queueing science perspective. J. Amer. Statist. Assoc. 100.Google ScholarCross Ref
- Busemann, S., Schmeier, S., and Arens, R. G. 2000. Message classification in the call center. In Proceedings of the 6thConference on Applied Natural Language Processing. Google ScholarDigital Library
- Chang, C.-C. and Lin, C.-J. Libsvm. http://www.csie.ntu.edu.tw/cjlin/libsvm/.Google Scholar
- Chu-Carroll, J. and Carpenter, B. 1999. Vector-Based natural language call routing. Comput. Ling. 25, 3, 361--388. Google ScholarDigital Library
- Cohen, W. W. 1995. Fast effective rule induction. In Proceedings of the 12thInternational Conference on Machine Learning. 115--123.Google ScholarCross Ref
- Cristani, M. and Cuel, R. 2005. A survey on ontology creation methodologies. Int. J. Sem. Web Inf. Syst. 1, 2, 49--69.Google ScholarCross Ref
- Ertoz, L., Steinbach, M., and Kumar, V. 2003. Finding clusters of different sizes, shapes, and densities in noisy, high dimensional data. In Proceedings of the 2ndSIAM International Conference on Data Mining. 349--358.Google Scholar
- Gamon, M. 2004. Sentiment classification on customer feedback data: Noisy data, large feature vectors, and the role of linguistic analysis. In Proceedings of the 20th International Conference on Computational Linguistics. Google ScholarDigital Library
- Gamon, M., Aue, A., Corston-Oliver, S., and Ringger, E. 2005. Pulse: Mining customer opinions from free text. Adv. Intell. Data Anal. 3646, 121--132. Google ScholarDigital Library
- Gans, N., Koole, G., and Mandelbaum, A. 2003. Telephone call centers: Tutorial, review, and research prospects. Manufact. Serv. Oper. Manag. 5, 79--141. Google ScholarDigital Library
- Ge, Y., Xiong, H., Zhou, W., Sahoo, R., Gao, X., and Wu, W. 2009. Multifocal learning and its application to customer service support. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 349--358. Google ScholarDigital Library
- Godbole, S. and Roy, S. 2008. An integrated system for automatic customer satisfaction analysis in the services industry. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 1076--1073. Google ScholarDigital Library
- Goel, V. and Byrne, W. 2000. Minimum bayes risk methods automatic speech recognition. Comput. Speech Lang. 14, 115--135.Google ScholarDigital Library
- Hui, S. C. and Jha, G. 2001. Application of data mining techniques for improving customer services. Int. J. Comput. Appl. Technol. 14, 64--77.Google ScholarCross Ref
- IBM. 2011. Ibm websphere voice server. www.ibm.com/software/pervasive/voice.Google Scholar
- Jarvis, R. A. and Patrick, E. A. 1973. Clustering using a similarity measure based on shared nearest neighbors. IEEE Trans. Comput. C-22, 11, 1025--1034. Google ScholarDigital Library
- Johansson, P. and Olhager, J. 2003. Industrial service profiling: Matching service offerings and processes. Int. J. Product. Econ. 3, 3.Google Scholar
- Karypis, G. 2011. Cluto. http://glaros.dtc.umn.edu/gkhome/views/cluto.Google Scholar
- Karypis, G., Han, E.-H. S., and Kumar, V. 1999. Chameleon: Hierarchical clustering using dynamic modeling. IEEE Comput. 32, 8, 68--75. Google ScholarDigital Library
- Paprzychi, M., Abraham, A., Guo, R., and Mukkamala, S. 2004. Data mining approach for analyzing call center performance. In Proceedings of the 17th International Conference on Innovations In Applied Artificial Intelligence. 1092--1101. Google ScholarDigital Library
- Porter, M. 1980. An algorithm for suffix stripping. Program 14, 3, 130--137.Google ScholarCross Ref
- Riccardi, G., Gorin, A., Ljolje, A., and Riley, M. 1997. A spoken language system for automated call routing. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'97). Google ScholarDigital Library
- Scholkopf, B. and Smola, A. J. 2002. Learning with Kernels. MIT Press, Cambridge, MA.Google Scholar
- Tan, P.-N., Steinbach, M., and Kumar, V. 2005. Introduction to Data Mining. Addison-Wesley. Google ScholarDigital Library
- Wu, J., Xiong, H., Wu, P., and Chen, J. 2007. Local decomposition for rare class analysis. In Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 814--823. Google ScholarDigital Library
- Wu, J., Xiong, H., and Chen, J. 2009. Adapting the right measures for k-means clustering. In Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 877--886. Google ScholarDigital Library
Index Terms
- Multifocal learning for customer problem analysis
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