TY - GEN
T1 - Multi-focal learning and its application to customer service support
AU - Ge, Yong
AU - Xiong, Hui
AU - Zhou, Wenjun
AU - Sahoo, Ramendra
AU - Gao, Xiaofeng
AU - Wu, Weili
PY - 2009
Y1 - 2009
N2 - 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.
AB - 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.
KW - Customer service support
KW - Multi-focal learning
UR - http://www.scopus.com/inward/record.url?scp=70350683536&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=70350683536&partnerID=8YFLogxK
U2 - 10.1145/1557019.1557062
DO - 10.1145/1557019.1557062
M3 - Conference contribution
AN - SCOPUS:70350683536
SN - 9781605584959
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 349
EP - 357
BT - KDD '09
T2 - 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '09
Y2 - 28 June 2009 through 1 July 2009
ER -