No abstract

In this paper a technique is proposed to tolerate missing values based on a system of fuzzy rules for classi cation. The presented method is mathematically solid but nevertheless easy and e cient to implement. Three possible applications of this methodology are outlined: the classi cation of patterns with an incomplete feature vector, the completion of the input vector when a certain class is desired, and the training or automatic construction of a fuzzy rule set based on incomplete training data. In contrast to a static replacement of the missing values, here the evolving model is used to predict the most possible values for the missing attributes. Benchmark datasets are used to demonstrate the capability of the presented approach in a fuzzy learning environment.

Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an ecient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs oer quick insights into the structure of the example data, that is, the underlying model. The underlying algorithm is demonstrated using several Java applets, which can be found under ÔElectronic annexesÕ on www.elsevier.com/locate/ida.

Methods to build function approximators from example data have gained considerable interest in the past. Especially methodologies that build models that allow an interpretation have attracted attention. Most existing algorithms, however, are either complicated to use or infeasible for high-dimensional problems. This article presents an ecient and easy to use algorithm to construct fuzzy graphs from example data. The resulting fuzzy graphs are based on locally independent fuzzy rules that operate solely on selected, important attributes. This enables the application of these fuzzy graphs also to problems in high dimensional spaces. Using illustrative examples and a real world data set it is demonstrated how the resulting fuzzy graphs oer quick insights into the structure of the example data, that is, the underlying model. The underlying algorithm is demonstrated using several Java applets, which can be found under ÔElectronic annexesÕ on www.elsevier.com/locate/ida.

No abstract