Learning maximal structure fuzzy rules with exceptions

Carmona, Pablo; Castro, Juan Luis; Zurita, J. M.

doi:10.1016/j.fss.2003.11.006

Cited by 12 publications

(5 citation statements)

References 8 publications

(5 reference statements)

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“…Several research about how minimized set of rules have been developed (Carmona, Castro, & Zurita, 2004;Castillo, Gonzalez, & Perez, 2001;Castro & Zurita, 1997;Castro, Castro-Schez, & Zurita, 1999;Castro, Castro-Schez, & Zurita, 2001). That means, no knowledge is eliminated from the rules in the minimization process, only transformed.…”

Section: Getting Minimal Sets Of Rulesmentioning

confidence: 99%

A fuzzy expert system for business management

Aranda

Castro

Navarro

et al. 2010

Expert Systems with Applications

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Section: Getting Minimal Sets Of Rulesmentioning

confidence: 99%

A fuzzy expert system for business management

Aranda

Castro

Navarro

et al. 2010

Expert Systems with Applications

Self Cite

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“…42 According to the classification proposed in Ref. 43, WM is a prototype-oriented strategy because it starts from examples and generates complete rules: Each rule considers all the available variables. These rules are likely to be simplified.…”

Section: Fuzzy Partition and Rule Base Designmentioning

confidence: 99%

HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism

Alonso

Magdalena

Guillaume³

2008

Int. J. Intell. Syst.

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This work describes a new methodology for making easier the design process of interpretable knowledge bases. It considers both expert knowledge and knowledge extracted from data. The combination of both kinds of knowledge is likely to yield robust compact systems with a good trade-off between accuracy and interpretability. Fuzzy logic offers an integration framework where both types of knowledge are represented using the same formalism. However, as two knowledge bases may convey contradictions and/or redundancies, the integration process must be made carefully. Results obtained, in four well-known benchmark classification problems, show that our methodology leads to highly interpretable knowledge bases with a good accuracy, comparable to that achieved by other methodologies. C 2008 Wiley Periodicals, Inc.

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“…Therefore, the interpretability improvement obtained when solving the inconsistency may involve a more complex rule set. This fact could be solved by considering a firing-level-based hierarchy of the rule set (being the more specific rules in an upper position) (Yager 1993), discounting the strength of the more general rules when they are inconsistent with more specific ones (Ishibuchi et al 2006), or even by extending the knowledge representation to consider rules with exceptions (Carmona et al 2004). These issues are out of the scope of this paper.…”

Section: Consistencymentioning

confidence: 99%

Learning consistent, complete and compact sets of fuzzy rules in conjunctive normal form for regression problems

2008

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When a flexible fuzzy rule structure such as those with antecedent in conjunctive normal form is used, the interpretability of the obtained fuzzy model is significantly improved. However, some important problems appear related to the interaction among this set of rules. Indeed, it is relatively easy to get inconsistencies, lack of completeness, redundancies, etc. Generally, these properties are ignored or mildly faced. This paper, however, focuses on the design of a multiobjective genetic algorithm that properly considers all these properties thus ensuring an effective search space exploration and generation of highly legible and accurate fuzzy models.

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Learning maximal structure fuzzy rules with exceptions

Cited by 12 publications

References 8 publications

A fuzzy expert system for business management

A fuzzy expert system for business management

HILK: A new methodology for designing highly interpretable linguistic knowledge bases using the fuzzy logic formalism

Learning consistent, complete and compact sets of fuzzy rules in conjunctive normal form for regression problems

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