Fuzzy Knnmodel Applied to Predictive Toxicology Data Mining
Fuzzy Knnmodel Applied to Predictive Toxicology Data Mining
复制标题
模糊Knn模型应用于预测毒理学数据挖掘
DOI：
10.1142/s1469026805001635
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发表时间：
2005
影响因子：
1.8
通讯作者：
Daniel Neagu
中科院分区：
文献类型：
--
作者：
Gongde Guo;Daniel Neagu
提出了一种稳健的化合物毒性预测方法--模糊kNN模型。该方法是基于监督聚类方法，称为kNNModel，它采用模糊分区，而不是清晰的分区组集群。模糊kNN模型的优点是两方面的：（1）它克服了为每个数据集选择参数e -允许的错误率和参数N -聚类覆盖的最小实例数的问题;（2）通过将它们分配给不同的聚类，使它们具有0和1之间的不同隶属度，更好地捕捉边界数据的特征。在UCI机器学习库的13个公共数据集和实际应用中的7个毒性数据集上进行的模糊kNN模型的实验结果与模糊c均值聚类、k均值聚类、kNN、模糊kNN和kNN模型的分类性能进行了比较。结果表明，模糊kNN模型是一种很有前途的化合物毒性预测方法。
A 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 e — 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.