Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems

Castro, Juan Luis; Castro-Schez, J. J.; Zurita, J. M.

doi:10.1016/s0165-0114(97)00105-x

Cited by 73 publications

(41 citation statements)

References 9 publications

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“…In the first example we have kept the discussion down to a minimum in order to give a description of how the FRT technique works rather to provide a solution to the complex problem to which is applied. Our focus in the second example is to show how a machine learning algorithm [9] uses the results obtained by the FRT. Next, we summarize a set of applications where the FRT is being successfully applied.…”

Section: Example Of the Suggested Methodsmentioning

confidence: 99%

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Fuzzy Repertory Table: A Method for Acquiring Knowledge About Input Variables to Machine Learning Algorithm

Castro-Schez

Castro

Zurita

2004

IEEE Trans. Fuzzy Syst.

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Abstract-In this paper, we develop a technique for acquiring the finite set of attributes or variables which the expert uses in a classification problem for characterising and discriminating a set of elements. This set will constitute the schema of a training data set to which an inductive learning algorithm will be applied. The technique developed uses ideas taken from psychology, in particular from Kelly's Personal Construct Theory. While we agree that Kelly's repertory grid technique is an efficient way to do this, it has several disadvantages which we shall try to solve by using a fuzzy repertory table. With the suggested technique, we aim to obtain the set of attributes and values which the expert can use to "measure" the object type (class) on the classification problem in some way. We will also acquire some general rules to identify the expert's evident knowledge; these rules will comprise concepts belonging to their conceptual structure.Index Terms-Expert systems, fuzzy repertory grid, knowledge acquisition, knowledge-based systems.

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Section: Example Of the Suggested Methodsmentioning

confidence: 99%

“…The distance between two groups of elements and will be calculated in (9). (9) The parameter takes its value from the [0,1] interval and will be defined by us.…”

Section: Algorithmmentioning

confidence: 99%

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Fuzzy Repertory Table: A Method for Acquiring Knowledge About Input Variables to Machine Learning Algorithm

Castro-Schez

Castro

Zurita

2004

IEEE Trans. Fuzzy Syst.

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“…On the other hand, the methods proposed in [10,26,43,68] obtain the most significative input variable for each rule during the learning process, instead of making an a priori variable selection.…”

Section: Selecting Input Variables In the Model And/or In The Linguismentioning

confidence: 99%

“…Moreover, the structure is a natural support to allow the absence of some input variables in each rule (simply making A i be the whole set of linguistic terms). Several learning methods have been proposed following this rule structure [10,[24][25][26]35,42,43,68].…”

Section: Alternative Linguistic Rule Expressionsmentioning

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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A fuzzy c-medians variant for the generation of fuzzy term sets

Liao

2002

Int. J. Intell. Syst.

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A fuzzy c-medians variant is proposed for the generation of fuzzy term sets with one half overlap. The proposed variant is modified from the original algorithm mainly in two areas. The first modification ensures that two end terms take the maximum and minimum domain values as their centers. The second modification prevents the generation of non-convex fuzzy terms that often are obtained by the original algorithm. The optimal number of terms and the optimal shape of membership function associated with each term are determined based on the mean squared error criterion. The exponential weight, m, used in the algorithm is found to greatly affect the shape of membership function. The effect of data size used for the generation of fuzzy terms is also discussed. A generalized s-shaped function with a tunable parameter along with its complement (i.e., a generalized z-shaped function with a tunable parameter) is developed to fit all term sets generated by the fuzzy c-medians variant using various m values.

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Learning maximal structure rules in fuzzy logic for knowledge acquisition in expert systems

Cited by 73 publications

References 9 publications

Fuzzy Repertory Table: A Method for Acquiring Knowledge About Input Variables to Machine Learning Algorithm

Fuzzy Repertory Table: A Method for Acquiring Knowledge About Input Variables to Machine Learning Algorithm

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

A fuzzy c-medians variant for the generation of fuzzy term sets

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