This paper describes the use of a neuro-fuzzy-genetic data mining architecture for finding hidden knowledge and modeling the data of the 1997 donation campaign of an American charitable organization. This data was used during the 1998 KDD Cup competition. In the architecture, all input variables are first preprocessed and all continuous variables are fuzzified. Principal component analysis (PCA) is then applied to reduce the dimensions of the input variables in finding combinations of variables, or factors, that describe major trends in the data. The reduced dimensions of the input variables are then used to train probabilistic neural networks (PNN) to classify the dataset according to the groups considered. A rule extraction technique is then applied in order to extract hidden knowledge from the trained neural networks and represent the knowledge in the form of crisp and fuzzy if-then-rules. In the final stage a genetic algorithm is used as a rule-pruning module to eliminate weak rules that are still in the rule base while insuring that the classification accuracy of the rule base is improved or not changed. The pruned rule base helps the charitable organization to maximize the donation and to understand the characteristics of the respondents of the direct mail fund raising campaign
K. Hemsathapat et al., "Using a Neuro-Fuzzy-Genetic Data Mining Architecture to Determine a Marketing Strategy in a Charitable Organization's Donor Database," Proceedings of the Change Management and the New Industrial Revolution (2001, Albany, NY), Institute of Electrical and Electronics Engineers (IEEE), Jan 2001.
The definitive version is available at http://dx.doi.org/10.1109/IEMC.2001.960482
Change Management and the New Industrial Revolution, 2001
Engineering Management and Systems Engineering
Keywords and Phrases
American Charitable Organization; KDD Cup Competition; Crisp If-Then-Rules; Data Mining; Data Modeling; Database Management Systems; Direct Mail Fund Raising Campaign; Donation Campaign; Fuzzy If-Then-Rules; Fuzzy Neural Nets; Genetic Algorithm; Genetic Algorithms; Hidden Knowledge; Input Variables; Knowledge Based Systems; Learning (Artificial Intelligence); Marketing; Neuro-Fuzzygenetic Data Mining Architecture; Principal Component Analysis; Probabilistic Neural Networks Training; Rule Base Classification Accuracy; Rule Extraction Technique; Rule-Pruning Module; Weak Rules Elimination
Article - Conference proceedings
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