![]() Vanthienen, J.: Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers. In: Journal of Machine Learning Research 1 (2000), 113–141.īaesens, B. Singer, Y.: Reducing multi-class to binary: A unifying approach for margin classifiers. of the 15th European Conference on Machine Learning. Japkowicz, N.: Applying Support Vector Machines to Imbalanced Datasets. The discussion provides a general understanding of technical and managerial challenges encountered in typical CRM applications and indicates promising areas for future research. To that end, a survey of the relevant literature is given to summarize the body of knowledge in each field and identify similarities across applications. Representative operational planning tasks are reviewed to describe the potential and limitations of classification analysis. This paper considers applications of classification within the scope of customer relationship management (CRM). Several empirical experiments in various domains including medical diagnosis, drug design, document and image classification as well as text recognition have proven its effectiveness to solve complex forecasting and identification tasks. ![]() Supervised classification embraces theories and algorithms for disclosing patterns within large, heterogeneous data streams.
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