Generalized Radial Basis Function Networks Trained with Instance Based Learning for Data Mining of Symbolic Data
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- 作者：Stergios Papadimitriou ; Seferina Mavroudi ; Liviu Vladutu and Anastasios Bezerianos
- 关键词：neural network learning ; data mining ; symbolic data classification ; radial basis functions ; heuristic learning
- 刊名：Applied Intelligence
- 出版年：2002
- 出版时间：2002
- 年：2002
- 卷：16
- 期：3
- 页码：223-234
- 全文大小：199 KB
文摘
The application of the Radial Basis Function neural networks in domains involving prediction and classification of symbolic data requires a reconsideration and a careful definition of the concept of distance between patterns. This distance in addition to providing information about the proximity of patterns should also obey some mathematical criteria in order to be applicable. Traditional distances are inadequate to access the differences between symbolic patterns. This work proposes the utilization of a statistically extracted distance measure for Generalized Radial Basis Function (GRBF) networks. The main properties of these networks are retained in the new metric space. Especially, their regularization potential can be realized with this type of distance. However, the examples of the training set for applications involving symbolic patterns are not all of the same importance and reliability. Therefore, the construction of effective decision boundaries should consider the numerous exceptions to the general motifs of classification that are frequently encountered in data mining applications. The paper supports that heuristic Instance Based Learning (IBL) training approaches can uncover information within the uneven structure of the training set. This information is exploited for the estimation of an adequate subset of the training patterns serving as RBF centers and for the estimation of effective parameter settings for those centers. The IBL learning steps are applicable to both the traditional and the statistical distance metric spaces and improve significantly the performance in both cases. The obtained results with this two-level learning method are significantly better than the traditional nearest neighbour schemes in many data mining problems.