2009 MultiFocalLearningandItsApplica

Subject Headings:

Notes

Categories and Subject Descriptors: H.2.8 Database Management: Database Applications — Data Mining; I.5.2 Pattern Recognition: Design Methodology — Classifier Design and Evaluation.
General Terms: Algorithms, Experimentation.

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 custormer problem.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 MultiFocalLearningandItsApplica	Hui Xiong Yong Ge Wenjun Zhou Ramendra Sahoo Xiaofeng Gao Weili Wu			Multi-focal Learning and Its Application to Customer Service Support		KDD-2009 Proceedings		10.1145/1557019.1557062		2009