A Spoken Language System for Automated Call Routing
ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97)-Volume 2 - Volume 2
Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
Vector-based natural language call routing
Computational Linguistics
Text Classification by Boosting Weak Learners based on Terms and Concepts
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Local decomposition for rare class analysis
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
An integrated system for automatic customer satisfaction analysis in the services industry
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Multifocal learning for customer problem analysis
ACM Transactions on Intelligent Systems and Technology (TIST)
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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.