The Combination and Comparison of Neural Networks with Decision Trees for Wine Classification. Rohitash Chandra, Kaylash Chaudhary, Akshay Kumar. IWIM, Prague, 2007.
Article (in pdf)
Abstract. This work presents the comparison and combination of neural networks with decision trees on the application of wine classification. Neural networks are first trained and then combined with decision trees in order to extract knowledge learnt in the training process. Artificial neural networks are used for the classification of Italian wines obtained from a region which has three different wine cultivars. Wines are classified according to their respective cultivar using the chemical analysis of the thirteen major chemical constituents. The trained network classifies a sample of wine according to the knowledge the network acquired by learning from previous wine samples. After successful training, knowledge is extracted from these trained networks using decision trees in the form of ‘if-then’ rules. We then use decision trees to train on the same dataset and compare the performance of neural networks, and decision trees in both knowledge extraction from neural networks and classification of wines on their own. Our results show that artificial neural networks perform better when compared to decision trees however, the extraction of knowledge from neural networks do not outperform the performance of decision trees alone. The general paradigm can be applied to other categories of food classification and processing.
Keywords. Wine classification, artificial neural networks, decision trees, and knowledge extraction.
References.
- Singhal P., Zhang J., Michel H.E., Singh B.R.: Fish-Quality Analysis using Artificial Neural Networks and Spectroscopic Data, Proceeding of the International Conference on Artificial Intelligence and Soft Computing, 2002.
- Pardo M., Sberveglieri G., Masulli F., Valentini G.: Decompositive classification models for electronic noses, Anal. Chimica Acta, 446, pp. 223-232, 2001.
- Patel V. C., McClеndon? R. W., Goodrum J. W.: Color Computer Vision and Artificial Neural Networks for the Detection of Defects in Poultry Eggs, Artificial Intelligence Review, pp. 163-176, 1998.
- Robinson A.J.: An application of recurrent nets to phone probability estimation, IEEE Transactions on Neural Networks, 5(2), pp. 298-305, 1994.
- Giles C.L., Lawrence S., Tsoi A.C.: Rule inference for financial prediction using recurrent neural networks, Proceedings of the IEEE/IAFE Computational Intelligence for Financial Engineering. New York City, USA, pp. 253-259, 1997.
- Marakami K., Taguchi H.: Gesture recognition using recurrent neural networks, Proceedings of the SIGCHI conference on Human factors in computing systems: Reaching through Technology, Louisiana, USA, pp. 237-242, 1991.
- Omlin C.W., Snyders S.: Inductive bias strength in knowledge-based neural networks: application to magnetic resonance spectroscopy of breast tissues, Artificial Intelligence in Medicine, 28(2), 2003.
- Chandra R., Omlin C. W.: Knowledge discovery using artificial neural networks for a conservation biology domain, Proceedings of the 2007 International Conference on Data Mining, Las Vegas, USA, In Press, 2007.
- Fu L.: Rule generation from neural networks, IEEE Transactions on Systems, Man and Cybernetics, 24(8), pp. 1114-1124, 1994.
