Guo, G.Neagu, Daniel2009-10-202009-10-202005Guo, G. and Neagu, D.C. (2005). Fuzzy kNNModel Applied to Predictive Toxicology Data Mining. International Journal of Computational Intelligence and Applications. Vol. 5, No. 3, pp. 321-333.http://hdl.handle.net/10454/3690NoA robust method, fuzzy kNNModel, for toxicity prediction of chemical compounds is proposed. The method is based on a supervised clustering method, called kNNModel, which employs fuzzy partitioning instead of crisp partitioning to group clusters. The merits of fuzzy kNNModel are two-fold: (1) it overcomes the problems of choosing the parameter ¿ ¿ allowed error rate in a cluster and the parameter N ¿ minimal number of instances covered by a cluster, for each data set; (2) it better captures the characteristics of boundary data by assigning them with different degrees of membership between 0 and 1 to different clusters. The experimental results of fuzzy kNNModel conducted on thirteen public data sets from UCI machine learning repository and seven toxicity data sets from real-world applications, are compared with the results of fuzzy c-means clustering, k-means clustering, kNN, fuzzy kNN, and kNNModel in terms of classification performance. This application shows that fuzzy kNNModel is a promising method for the toxicity prediction of chemical compounds.enFuzzy kNNModelClassificationPredictive toxicologyArticlehttps://doi.org/10.1142/S1469026805001635