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An interesting and little explored way to understand data is based on prototype rules (P-rules). The goal of this approach is to find optimal similarity (or distance) functions and position of prototypes to which unknown vectors are compared. In real applications similarity functions frequently involve different types of attributes, such as continuous,(More)
A comparison between five feature ranking methods based on entropy is presented on artificial and real datasets. Feature ranking method using /spl chi//sup 2/ statistics gives results that are very similar to the entropy-based methods. The quality of feature rankings obtained by these methods is evaluated using the decision tree and the nearest neighbor(More)
Prototype-based rules are an interesting alternative to fuzzy and crisp logical rules, in many cases providing simpler, more accurate and more com-prehensible description of the data. Such rules may be directly converted to fuzzy rules. A new algorithm for generation of prototype-based rules is introduced and a comparison with results obtained by neurofuzzy(More)
Probabilistic distance functions, including several variants of value difference metrics, minimum risk metric and Short-Fukunaga metrics, are used with prototype-based rules (P-rules) to provide a very concise and comprehensible classification model. Application of probabilistic metrics to nominal or discrete features is straightforward. Heterogeneous(More)
This paper presents the application of neural networks in the design process of new technologies taking into account factors such as their influence on the environment and the economic effects of their implementation. The use of neural networks allowed eco-efficiency assessment of technologies based on highly reduced number of descriptive design parameters,(More)
This paper presents a neural network tree regression system with dynamic optimization of input variable transformations and post-training optimization. The decision tree consists of MLP neural networks, which optimize the split points and at the leaf level predict final outputs. The system is designed for regression problems of big and complex datasets. It(More)
– Many different approaches to the problem of classification have been collected. An interesting way to understand data leads to prototype rules (P-rules). In this approach the aim is to find optimal position of prototypes to which we compare unknown vectors. One of important problems in applications P-rules for real datasets are distance functions(More)
In this paper we compare different evolutionary algorithm approaches and parameters used to optimize the output of neural network committee trained on regression problems. This is especially useful for large and complex datasets. We used the methodology presented in this paper to optimize the output of the committee to predict the temperature in the(More)