Extracting underlying meaningful features and canceling noise using independent component analysis for direct marketing

As the Internet spreads widely, it has become easier for companies to obtain and utilize valuable information on their customers. Nevertheless, many of them have difficulty in using the information effectively because of the huge amount of data from their customers that must to be analyzed. In addition, the data usually contains much noise due to anonymity of the Internet. Consequently, extracting the underlying meanings and canceling the noise of the collected customer data are crucial for the companies to implement their strategies for customer relationship management. As a novel solution, we propose the use of independent component analysis (ICA). ICA is a multivariate statistical tool which extracts independent components or sources of information, given only observed data that are assumed to be linear mixtures of some unknown sources. Moreover, ICA is able to reduce the dimension of the observed data, especially noisy variables. To validate the usefulness of ICA, we applied it to a real-world one-to-one marketing case. In this study, we used ICA as a preprocessing tool, and made a prediction for potential buyers using artificial neural networks (ANNs). We also applied PCA as a comparative model for ICA. The experimental results showed that ICA-preprocessed ANN outperformed all the comparative classifiers without preprocessing as well as PCA-preprocessed ANN. (c) 2006 Elsevier Ltd. All rights reserved.
Publisher
PERGAMON-ELSEVIER SCIENCE LTD
Issue Date
2007-07
Language
ENG
Keywords

BLIND SOURCE SEPARATION; PCA; ALGORITHMS; REDUCTION; ICA

Citation

EXPERT SYSTEMS WITH APPLICATIONS, v.33, no.1, pp.181 - 191

ISSN
0957-4174
DOI
10.1016/j.eswa.2006.04.016
URI
http://hdl.handle.net/10203/3670
Appears in Collection
KGSF-Journal Papers(저널논문)
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