Adaptive Logic Networks in Bank Customer Modeling
نویسنده
چکیده
The goal of EUNITE’s second annual competition was to classify bank customers as either active or non-active (binary response variable) based on 36 attributes, 30 of which were numerical, 6 of which were combinatorial, some having more than 50 possible values. The meanings of the variables were kept secret. Example data was made available, consisting of 12000 customer records, each featuring the 36 attributes together with the instantiated response variable. The implicit sub-goal was to develop a model through data-driven (e.g. machine learning) techniques. A further data set of 12000 records was provided, consisting of just the attributes, for assessment purposes. It would then be for the competition organizers to assess the correctness of results. Several types of algorithm were briefly investigated, out of which Adaptive Logic Networks (ALNs) appeared to perform best – provided care was taken with regard to selecting driver variables and optimizing learning parameters.
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تاریخ انتشار 2002