Data Mining Methods and Models
Data Mining Methods and Models
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Modeling wine preferences by data mining from physicochemical properties
Decision Support Systems
Data mining with an ant colony optimization algorithm
IEEE Transactions on Evolutionary Computation
Classification With Ant Colony Optimization
IEEE Transactions on Evolutionary Computation
ACC'11/MMACTEE'11 Proceedings of the 13th IASME/WSEAS international conference on Mathematical Methods and Computational Techniques in Electrical Engineering conference on Applied Computing
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This paper is dedicated to the application of swarm intelligence in the field of data mining. An Ant Colony Optimization (ACO) logistic regression model is presented with applications for wine quality assessment. The proposed ACO model may be designed to minimize either the mean absolute regression error (MAE) or the mean square regression error (MSE). The method is evaluated using the Wine Quality database (red wine) with 1599 11-dimensional samples provided by UCI Machine Learning Repository. The input features correspond to 11 physicochemical wine tests and the quality scores belong to the set {3, 4, 5, 6, 7, 8}. The best simulation variants of ACO logistic regression model have led to better performances than the classical Multiple Linear Regression (MLR) technique.