ABSTRACT
In this study, we formalize a multi-focal learning problem, where training data are partitioned into several different focal groups and the prediction model will be learned within each focal group. The multi-focal 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. The experienced customers usually give more precise and focused descriptions about the problem. In contrast, the inexperienced customers usually provide more diverse descriptions. In this case, the examples from the same class in the training data can be naturally in different focal groups. As a result, it is necessary to identify those natural focal groups and exploit them for learning at different focuses. The key developmental challenge is how to identify those focal groups in the training data. As a case study, we exploit multi-focal learning for profiling problems in customer service centers. The results show that multifocal learning can significantly boost the learning accuracies of existing learning algorithms, such as Support Vector Machines (SVMs), for classifying customer problems.
Supplemental Material
- S. Bloehdorn and A. Hotho. Text classification by boosting weak learners based on terms and concepts. In ICDM, 2004. Google ScholarDigital Library
- C.-C. Chang and C.-J. Lin. Libsvm: http://www.csie.ntu.edu.tw/ cjlin/libsvm/.Google Scholar
- J. Chu-Carroll and B. Carpenter. Vector-based natural language call routing. Computational Linguistics, 1999. Google ScholarDigital Library
- N. Gans., G.Koole., and A.Mandelbaum. Telephone call centers: Tutorial, review, and research prospects. Manufacturing Service Oper. Management, 2003. Google ScholarDigital Library
- G.Karypis, E.-H.S.Han, and V.Kumar. Chameleon: Hierarchical clustering using dynamic modeling. Computer, 1999. Google ScholarDigital Library
- S. Godbole and S. Roy. An integrated system for automatic customer satisfaction analysis in the services industry. In SIGKDD, 2008. Google ScholarDigital Library
- V. Goel and W. Byrne. Minimum bayes risk methods automatic speech recognition. Computer Speech and Language, 14(2):115--135, 2000.Google ScholarDigital Library
- P. Johansson and J. Olhager. Industrial service profiling: Matching service offerings and processes. International Journal of Production Economics, 2003.Google Scholar
- J.Wu, H.Xiong, and et al. Local decomposition for rare class analysis. In SIGKDD, 2007. Google ScholarDigital Library
- G. Karypis. Cluto: http://glaros.dtc.umn.edu/gkhome/views/cluto.Google Scholar
- L.Brown and et al. Statistical analysis of a telephone call center: a queueing science perspective. Technical report, The Wharton School, 2002.Google Scholar
- M.Cristani and R.Cuel. A survey on ontology creation methodologies. International Journal on Semantic Web and Information Systems, 2005.Google ScholarCross Ref
- M. Porter. An algorithm for suffix stripping. Program, 1980.Google ScholarCross Ref
- G. Riccardi., A. Gorin., A. Ljolje, and M. Riley. A spoken language system for automated call routing. In ICASSP, 1997. Google ScholarDigital Library
- B. Scholkopf and A. J. Smola. Learning with Kernels. MIT Press, Cambridge, MA, 2002.Google Scholar
- I. W. V. Server. www.ibm.com/software/pervasive/voice server.Google Scholar
- P.-N. Tan, M. Steinbach, and V. Kumar. Introduction to Data Mining. Addison-Wesley, 2005. Google ScholarDigital Library
- W.Cohen. Fast effective rule induction. In ICML, 1995.Google ScholarDigital Library
Index Terms
- Multi-focal learning and its application to customer service support
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