Abstract
The increasing amount and complexity of data in toxicity prediction calls for new approaches based on hybrid intelligent methods for mining the data. This focus is required even more in the context of increasing number of different classifiers applied in toxicity prediction. Consequently, there exist a need to develop tools to integrate various approaches. The goal of this research is to apply neuro-fuzzy networks to provide an improvement in combining the results of five classifiers applied in toxicity of pesticides. Nevertheless, fuzzy rules extracted from the trained developed networks can be used to perform useful comparisons between the performances of the involved classifiers. Our results suggest that the neuro-fuzzy approach of combining classifiers has the potential to significantly improve common classification methods for the use in toxicity of pesticides characterization, and knowledge discovery.
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References
Benfenati, E., Pelagatti, S., Grasso, P., Gini, G.: COMET: the approach of a project in evaluating toxicity. Gini, G. C.; Katritzky, A. R. (eds.): Predictive Toxicology of Chemicals: Experiences and Impact of AI Tools. AAAI 1999 Spring Symposium Series. AAAI Press, Menlo Park, CA (1999) 40–43
Benfenati, E., Piclin, N., Roncaglioni, A., Varì, M.R.: Factors Influencing Predictive Models For Toxicology. SAR and QSAR in environmental research, 12 (2001) 593–603
Bishop, C.M.: Neural networks for pattern recognition. Clarendon Press, Oxford (1995)
Chen, K., Chi, H.: A method of combining multiple probabilistic classifiers through soft competition on different feature sets. Neurocomputing 20 (1998) 227–252
Duin, R.P.W., Tax, D.M.J.: Experiments with Classifier Combining Rules. Lecture Notes in Computer Science, Vol. 1857. Springer-Verlag, Berlin (2000) 16–29
Enbutsu, I., Baba, K., Hara, N.: Fuzzy Rule Extraction from a Multilayered Network. Procs. of IJCNN’91, Seattle (1991) 461–465
Gini, G., Benfenati, E., Boley, D.: Clustering and Classification Techniques to Assess Aquatic Toxicity. Procs. of the Fourth Int’l Conf. KES2000, Brighton, UK, Vol. 1 (2000) 166–172
Gini, G., Lorenzini, M., Benfenati, E., Brambilla, R., Malvé, L.: Mixing a Symbolic and a Subsymbolic Expert to Improve Carcinogenicity Prediction of Aromatic Compounds. Kittler, J., Roli, F. (eds.): Multiple Classifier Systems. Springler-Verlag, Berlin (2001) 126–135
Helma, C., Gottmann, E., Kramer, S.: Knowledge discovery and data mining in toxicology. Statistical methods in medical research, 9 (2000) 131–135
Ho, T., Hull, J., Srihari, S.: Decision combination in multiple classifier systems. IEEE Trans. Pattern Anal. Machine Intelligence 16/1 (1994) 66–75
Jacobs, R.A.: Methods for combining experts’ probability assessments. Neur. Comp. 7/5(1995)867–888
Jagielska, I., Matthews, C., Whitfort, T.: An investigation into the application of ANN, FL, GA, and rough sets to automated knowledge acquisition for classification problems. Neurocomp, 24(1999)37–54
Kosko, B.: Neural Networks and Fuzzy System. Prentice-Hall, Englewood Cliffs (1992)
Lin, C.T., George Lee, C.S.: Neural-Network Based Fuzzy Logic Control and Decision System. IEEE Transactions on Computers, 40/12 (1991) 1320–1336
Nauck, D., Kruse, R.: NEFCLASS-X: A Neuro-Fuzzy Tool to Build Readable Fuzzy Classifiers. BT Tech. J. 16/3 (1998) 180–192
Neagu, C.-D., Avouris, N.M., Kalapanidas, E., Palade, V.: Neural and Neuro-fuzzy Integration in a Knowledge-based System for Air Quality Prediction. App Intell. J. (2001 accepted)
Palade, V., Neagu, C.-D., Patton, R.J.: Interpretation of Trained Neural Networks by Rule Extraction, Procs. of Int’l Conf. 7th Fuzzy Days in Dortmund (2001) 152–161
Rumelhart, D.E., McClelland, J.L.: Parallel Distributed Processing, Explanations in the Microstructure of Cognition. MIT Press (1986)
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Benfenati, E., Mazzatorta, P., Neagu, D., Gini, G. (2002). Combining Classifiers of Pesticides Toxicity through a Neuro-fuzzy Approach. In: Roli, F., Kittler, J. (eds) Multiple Classifier Systems. MCS 2002. Lecture Notes in Computer Science, vol 2364. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45428-4_29
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DOI: https://doi.org/10.1007/3-540-45428-4_29
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