Abstract: This paper presents an exploratory study of the effectiveness of support vector machines in the prediction of a product quality based on its characteristics. The study answers the following three questions: how does the choice of kernel and model parameters affect the predictive abilities of support vector machines; can an alternative subset of variables be unearthed that can be used in order to increase the predictive abilities of the data mining model; how will the removal of potential outliers affect the predictive abilities of the data mining model. We used a dataset of red and white wine samples presented by their physiochemical characteristics. Findings show that a correct selection of kernel and appropriate variable selection technique may have a significant impact on the prediction ability of the data mining model. Certain model settings can even make it to outperform the best technique reported thus far in the application area.

Keywords: data mining, support vector machines, sensory preferences, variable selection, wine classification.

ACM Classification Keywords: I.5.2- Computing Methodologies - Pattern Recognition – Design Methodology - Classifier design and evaluation.

Link:

PRODUCT QUALITY ANALYSIS USING SUPPORT VECTOR MACHINES

A. Nachev, B. Stoyanov

http://www.foibg.com/ijima/vol01/ijima01-2-p10.pdf