From approximative to descriptive fuzzy classifiers

Marín-Blázquez, Javier G.; Shen, Qiang

doi:10.1109/tfuzz.2002.800687

Cited by 79 publications

(38 citation statements)

References 23 publications

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“…As most data contain real-valued features, it is necessary to perform a discretization step beforehand to use RSFS. This is typically implemented by standard fuzzification techniques [21]. However, membership degrees of feature values to fuzzy sets are not exploited in the process of dimensionality reduction when using RSFS.…”

Section: Fuzzy-rough Feature Selectionmentioning

confidence: 99%

Approximation-based feature selection and application for algae population estimation

Shen

Jensen

2007

Appl Intell

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This paper presents a data-driven approach for feature selection to address the common problem of dealing with high-dimensional data. This approach is able to handle the real-valued nature of the domain features, unlike many existing approaches. This is accomplished through the use of fuzzy-rough approximations. The paper demonstrates the effectiveness of this research by proposing an estimator of algae populations, a system that approximates, given certain water characteristics, the size of algae populations. This estimator significantly reduces computer time and space requirements, decreases the cost of obtaining measurements and increases runtime efficiency, making itself more viable economically. By retaining only information required for the estimation task, the system offers higher accuracy than conventional estimators. Finally, the system does not alter the domain semantics, making any distilled knowledge human-readable. The paper describes the problem domain, architecture and operation of the system, and provides and discusses detailed experimentation. The results show that algae estimators using a fuzzy-rough feature selection step produce more accurate predictions of algae populations in general.

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Section: Fuzzy-rough Feature Selectionmentioning

confidence: 99%

Approximation-based feature selection and application for algae population estimation

Shen

Jensen

2007

Appl Intell

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“…Note that the definition of traditional hedges does not produce substantial changes in these types of fuzzy sets. To address this issue, a new definition of hedges has been proposed in [19,20]. This new approach produces better results when applied to linear piece-wise sets.…”

Section: Precise and Linguistic Fuzzy Modelingmentioning

confidence: 99%

“…This paper uses the exact same definition of such hedges in implementation. Therefore, detailed hedge definitions are omitted herein but can be found in [19,20].…”

Section: Precise and Linguistic Fuzzy Modelingmentioning

confidence: 99%

“…The original fuzzy sets are not changed, but the hedged fuzzy sets provide modifiable means of modelling a given problem, and therefore more freedom in representing knowledge in the domain. In previous research genetic algorithms have been applied to obtaining compact sets of linguistically hedged fuzzy rules from data, by translating precise models [20] into linguistic ones. In this work a classifier system (XCS [31]) will be used for fuzzy rule extraction.…”

Section: Introductionmentioning

confidence: 99%

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A Fuzzy-XCS Classifier System with Linguistic Hedges

Marín-Blázquez

Shen²

2008

IJCIR

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Abstract:Many real-world problems require the development and application of algorithms that automatically generate human interpretable knowledge from historical data. Most existing algorithms for rule induction from imprecise data have followed the precise approach, where definitions of the fuzzy sets that are intended to capture certain vague concepts are allowed to be modified such that they fit the data. These approaches typically destroy the original semantics or meaning of the given fuzzy sets, which often leads to loss of transparency in the resulting model or models. In order to overcome this fundamental limitation, a descriptive approach has been proposed in which human defined fuzzy sets are not allowed to be modified. However, as the fuzzy set definitions cannot be modified, and only a small number of them are normally available, only a limited number of possible rules are derivable. Such rules are not very flexible and in many cases, will not necessarily fit the data well. To address this important issue, at least partially, linguistic hedges have been introduced to provide a more adaptable means of learning from data, thereby offering more flexibility in domain knowledge representation and extraction. Following this approach, this paper presents a novel rule induction mechanism which extends a classifier system (XCS) by employing linguistic hedges. The resultant fuzzy XCS classifier with linguistic hedges is evaluated against a real-world forensic glass classification problem. The results demonstrate that the inclusion of hedges to support finer granularity in linguistic fuzzy modelling improves the accuracy of the resulting classifiers, whilst simultaneously preserving the interpretability of the learned models. This approach not only offers the user rules to decide on classes, but also rules to decide which classes to discard. It also inherits from XCS, the ability to deal with data that involves imbalanced classes.

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“…Many fuzzy rule induction algorithms have been established, mostly for deriving a concise and humancomprehensible set of rules for tasks like classification and prediction. These include, for example, fuzzy association rule mining [4], [29], first-order fuzzy rule generation [8], [22], and linguistic semantics-preserving modeling [20], [24]. However, the efficacy of most of the existing approaches to fuzzy rule induction reduces as the data dimensionality increases.…”

Section: Introductionmentioning

confidence: 99%

Hybrid fuzzy-rough rule induction and feature selection

Jensen

Cornelis

Shen

2009

2009 IEEE International Conference on Fuzzy Systems

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Abstract-The automated generation of feature patternbased if-then rules is essential to the success of many intelligent pattern classifiers, especially when their inference results are expected to be directly human-comprehensible. Fuzzy and rough set theory have been applied with much success to this area as well as to feature selection. Since both applications of rough set theory involve the processing of equivalence classes for their successful operation, it is natural to combine them into a single integrated method that generates concise, meaningful and accurate rules. This paper proposes such an approach, based on fuzzy-rough sets. The algorithm is experimentally evaluated against leading classifiers, including fuzzy and rough rule inducers, and shown to be effective.

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From approximative to descriptive fuzzy classifiers

Cited by 79 publications

References 23 publications

Approximation-based feature selection and application for algae population estimation

Approximation-based feature selection and application for algae population estimation

A Fuzzy-XCS Classifier System with Linguistic Hedges

Hybrid fuzzy-rough rule induction and feature selection

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