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A Hybrid Multilayer Perceptron Neural Network for Direct Marketing
Abstract
Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.
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