Abstract
In accordance with advancement in society, consumers’ psychological feelings constitute an important factor whereby manufacturers need to consider when designing or improving various services and/or products in order to gain a competitive edge in global market. In this study, a hybrid model combining the fuzzy min–max (FMM) neural network and the classification and regression tree (CART) is applied to extract useful information from databases pertaining to products and/or services. The hybrid model, known as FMM–CART, exploits the advantages of both FMM and CART in undertaking data classification and knowledge discovery problems. It is able to categorize products/services into different classes (through FMM) and, at the same time, to provide useful information of the product/service features in each class (through CART). To demonstrate the usefulness of FMM–CART in affective engineering (AE) applications, two publicly available data sets related to the automobile industry are utilized. The experimental outcome positively indicates the potential of FMM–CART in classifying products and/or services and elucidating useful knowledge and information pertaining to the classification process.
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Seera, M., Lim, C.P., Watada, J. (2014). Understanding Product Features Using a Hybrid Machine Learning Model. In: Watada, J., Shiizuka, H., Lee, KP., Otani, T., Lim, CP. (eds) Industrial Applications of Affective Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-04798-0_22
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DOI: https://doi.org/10.1007/978-3-319-04798-0_22
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