Extraction of crisp logical rules using constructive constrained backpropagation networks

Duch, Włodzisław; Adamczak, Rafał; Grąbczewski, Krzysztof

doi:10.1109/icnn.1997.614440

Cited by 16 publications

(29 citation statements)

References 3 publications

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“…Therefore discovering the proper bias for a given data is very important. Some real world examples showing the differences between RBF and MLP networks that are mainly due to the transfer functions used were presented in [57] and [46,47].…”

Section: Heterogeneous Adaptive Systemsmentioning

confidence: 99%

Towards Comprehensive Foundations of Computational Intelligence

Duch

2007

Studies in Computational Intelligence

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Abstract. Although computational intelligence (CI) covers a vast variety of different methods it still lacks an integrative theory. Several proposals for CI foundations are discussed: computing and cognition as compression, meta-learning as search in the space of data models, (dis)similarity based methods providing a framework for such meta-learning, and a more general approach based on chains of transformations. Many useful transformations that extract information from features are discussed. Heterogeneous adaptive systems are presented as particular example of transformation-based systems, and the goal of learning is redefined to facilitate creation of simpler data models. The need to understand data structures leads to techniques for logical and prototype-based rule extraction, and to generation of multiple alternative models, while the need to increase predictive power of adaptive models leads to committees of competent models. Learning from partial observations is a natural extension towards reasoning based on perceptions, and an approach to intuitive solving of such problems is presented. Throughout the paper neurocognitive inspirations are frequently used and are especially important in modeling of the higher cognitive functions. Promising directions such as liquid and laminar computing are identified and many open problems presented.

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Section: Heterogeneous Adaptive Systemsmentioning

confidence: 99%

Towards Comprehensive Foundations of Computational Intelligence

Duch

2007

Studies in Computational Intelligence

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“…Previously [1]- [3] we have described a complete methodology of rule extraction from the data. It is composed from the following steps:…”

Section: Application and Optimization Of Rule-based Classifiersmentioning

confidence: 99%

“…A solution to these problems facing crisp and fuzzy rule-based classifiers applied to data with continuous features is presented in this paper. Neural and machinelearning methods of rule extraction from data were described in our previous publications [1]- [3]. Therefore we will assume that a small number of crisp logical rules has already been found.…”

Section: Introductionmentioning

confidence: 99%

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Optimization and Interpretation of Rule-Based Classifiers

Duch

Jankowski

Grąbczewski

et al. 2000

Intelligent Information Systems

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Abstract. Machine learning methods are frequently used to create rule-based classifiers. For continuous features linguistic variables used in conditions of the rules are defined by membership functions. These linguistic variables should be optimized at the level of single rules or sets of rules. Assuming the Gaussian uncertainty of input values allows to increase the accuracy of predictions and to estimate probabilities of different classes. Detailed interpretation of relevant rules is possible using (probabilistic) confidence intervals. A real life example of such interpretation is given for personality disorders. The approach to optimization and interpretation described here is applicable to any rule-based system.

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“…N EURAL methodology of crisp logical rule extraction developed by our group has been described in a series of papers [1], [2], [4], [10], [13], therefore only a very brief summary is given here.…”

Section: Neural Rule Extraction Methodologymentioning

confidence: 99%