A Hybrid Multilayer Perceptron Neural Network for Direct Marketing

A Hybrid Multilayer Perceptron Neural Network for Direct Marketing

M. Govindarajan, RM. Chandrasekaran
Copyright: © 2012 |Pages: 11
DOI: 10.4018/ijkbo.2012070104
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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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Hybrid models and combined models, terms often used synonymously, have been developed to improve prediction accuracy by using several supervised learning methods together. Some studies on hybrid or combined models utilize different supervised learning methods sequentially. For example, Coenen, Swinnen, Vanhoof, and Wets (2000) propose a hybrid model to improve the response rate of direct mailing. Also, Hsu, Lai, Chui, and Hsu (2003) have studied the learning capability improvement of students using a hybrid model that was a mixture of the optimal tree model disclosed by association analysis with categorical variables and the tree model directly applied to continuous variables.

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