Rule-based modeling: precision and transparency

Setnes, M.; Babuška, Robert; Verbruggen, H.B.

doi:10.1109/5326.661100

Cited by 257 publications

(99 citation statements)

References 14 publications

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“…In the examples in this paper, the antecedents of the initial fuzzy rule bases are obtained from fuzzy -means clustering in the product space of the sampled input-output data. Following the approach in [3], [15], each cluster represents a certain region in the systems input-output state-space and corresponds to a rule in the rule base. The fuzzy sets in the rule antecedent are obtained by projecting the cluster onto the domain of the various inputs.…”

Section: B Identification From Datamentioning

confidence: 99%

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Compact and transparent fuzzy models and classifiers through iterative complexity reduction

Roubos

Setnes

2001

IEEE Trans. Fuzzy Syst.

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241

122

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Abstract-In our previous work we showed that genetic algorithms (GAs) provide a powerful tool to increase the accuracy of fuzzy models for both systems modeling and classification. In addition to these results, we explore the GA to find redundancy in the fuzzy model for the purpose of model reduction. An aggregated similarity measure is applied to search for redundancy in the rule base description. As a result, we propose an iterative fuzzy identification technique starting with data-based fuzzy clustering with an overestimated number of local models. The GA is then applied to find redundancy among the local models with a criterion based on maximal accuracy and maximal set similarity. After the reduction steps, the GA is applied with another criterion searching for minimal set similarity and maximal accuracy. This results in an automatic identification scheme with fuzzy clustering, rule base simplification and constrained genetic optimization with low-human intervention. The proposed modeling approach is then demonstrated for a system identification and a classification problem. Results are compared to other approaches in the literature. Attractive models with respect to compactness, transparency and accuracy, are the result of this symbiosis.Index Terms-Fuzzy classifier, genetic algorithm (GA), Iris data, rule base reduction, Takagi-Sugeno (T-S) fuzzy model, transparency and accuracy.

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Section: B Identification From Datamentioning

confidence: 99%

“…Most approaches, however, utilize only the function approximation capabilities of fuzzy systems and little attention is paid to the qualitative aspects. This makes them less suited for applications in which emphasis is not only on accuracy, but also on interpretability, computational complexity, and maintainability [7]- [11].…”

mentioning

confidence: 99%

Compact and transparent fuzzy models and classifiers through iterative complexity reduction

Roubos

Setnes

2001

IEEE Trans. Fuzzy Syst.

Self Cite

241

122

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“…This concept is defined as a property that enables us to understand the influence of each system parameter on the system output. It has been mainly applied to neuro-fuzzy systems (Brown & Harris, 1994) and Takagi-Sugeno models (Setnes, Babuska & Verbruggen, 1998).…”

Section: Knowledge Data and Interpretabilitymentioning

confidence: 99%

Expert guided integration of induced knowledge into a fuzzy knowledge base

Guillaume¹,

Magdalena

2006

Soft Comput

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This paper proposes a method for building accurate and interpretable systems by integrating expert and induced knowledge into a single knowledge base. To favor the cooperation between expert knowledge and data, the induction process is run under severe constraints to ensure the fully control of the expert. The procedure is made up of two hierarchical steps. Firstly, a common fuzzy input space is designed according to both the data and expert knowledge. The compatibility of the two types of partitions, expert and induced, is checked according to three criteria: range, granularity and semantic interpretation. Secondly, expert rules and induced rules are generated according to the previous common fuzzy input space. Then, induced and expert rules have to be merged into a new rule base. Thanks to the common universe resulting from the first step, rule comparison can be made at the linguistic level only. The possible conflict situations are managed and the most important rule base features, consistency, redundancy and completeness, are studied. The first step is thoroughly described in this paper, while the second is only introduced.

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“…Nevertheless, a new tendency in the FM scientific community that looks for a good balance between interpretability and accuracy is increasing in importance [3,9,54,65]. The aim of this chapter is to review some of the recent proposals that attempt to address this issue using mechanisms to improve the interpretability of fuzzy models.…”

Section: Introductionmentioning

confidence: 99%

Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview

Casillas

Cordón

Herrera

et al. 2003

Interpretability Issues in Fuzzy Modeling

114

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Abstract. System modeling with fuzzy rule-based systems (FRBSs), i.e. fuzzy modeling (FM), usually comes with two contradictory requirements in the obtained model: the interpretability, capability to express the behavior of the real system in an understandable way, and the accuracy, capability to faithfully represent the real system. While linguistic FM (mainly developed by linguistic FRBSs) is focused on the interpretability, precise FM (mainly developed by Takagi-Sugeno-Kang FRBSs) is focused on the accuracy. Since both criteria are of vital importance in system modeling, the balance between them has started to pay attention in the fuzzy community in the last few years.The chapter analyzes mechanisms to find this balance by improving the interpretability in linguistic FM: selecting input variables, reducing the fuzzy rule set, using more descriptive expressions, or performing linguistic approximation; and in precise FM: reducing the fuzzy rule set, reducing the number of fuzzy sets, or exploiting the local description of the rules.

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Rule-based modeling: precision and transparency

Cited by 257 publications

References 14 publications

Compact and transparent fuzzy models and classifiers through iterative complexity reduction

Compact and transparent fuzzy models and classifiers through iterative complexity reduction

Expert guided integration of induced knowledge into a fuzzy knowledge base

Interpretability Improvements to Find the Balance Interpretability-Accuracy in Fuzzy Modeling: An Overview

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