An Adaptable Gaussian Neuro-Fuzzy Classifier

Pertselakis, Minas; Frossyniotis, Dimitrios S.; Stafylopatis, Andreas

doi:10.1007/3-540-44989-2_110

“…A learning algorithm is then applied to fine tune the rules based on the available training data. These approaches usually search for hyperellipsoidal or hyperrectangular clusters in input space and are shown to typically produce rules which are hard to interpret [21,22].…”

Section: Rule Antecedent Structurementioning

confidence: 99%

Incremental Local Linear Fuzzy Classifier in Fisher Space

Eftekhari¹,

Moghaddam²,

Forouzanfar³

et al. 2022

Preprint

0

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Optimizing the antecedent part of neurofuzzy system is an active research topic, for which diﬀerent approaches have been developed. However, current approaches typically suﬀer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classiﬁers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classiﬁer is then built in the transformed space, starting from the simplest form (a global linear classiﬁer). If the overall performance of the classiﬁer was not satisfactory, it would be iteratively reﬁned by incorporating additional local classiﬁers. In addition, rule consequent parameters are optimized using a local least square approach. Our reﬁnement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identiﬁcation problems. The proposed classiﬁer is compared to several benchmark classiﬁers on a number of well-known datasets. The results prove the eﬃcacy of the proposed classiﬁer in achieving high performance while incurring low computational eﬀort.

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“…A learning algorithm is then applied to fine tune the rules based on the available training data. These approaches usually search for hyperellipsoidal or hyperrectangular clusters in input space and are shown to typically produce rules which are hard to interpret [21,22].…”

Section: Rule Antecedent Structurementioning

confidence: 99%

Incremental Local Linear Fuzzy Classifier in Fisher Space

Eftekhari¹,

Moghaddam²,

Forouzanfar³

et al. 2022

Preprint

0

View full text Add to dashboard Cite

Optimizing the antecedent part of neurofuzzy system is an active research topic, for which diﬀerent approaches have been developed. However, current approaches typically suﬀer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classiﬁers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classiﬁer is then built in the transformed space, starting from the simplest form (a global linear classiﬁer). If the overall performance of the classiﬁer was not satisfactory, it would be iteratively reﬁned by incorporating additional local classiﬁers. In addition, rule consequent parameters are optimized using a local least square approach. Our reﬁnement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identiﬁcation problems. The proposed classiﬁer is compared to several benchmark classiﬁers on a number of well-known datasets. The results prove the eﬃcacy of the proposed classiﬁer in achieving high performance while incurring low computational eﬀort.

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“…A learning algorithm is then applied to fine tune the rules based on the available training data. These approaches usually search for hyperellipsoidal or hyperrectangular clusters in input space and are shown to typically produce rules which are hard to interpret [21,22]. Partitioning-based methods such as NEFCLASS [9] divide the input space into finer regions by grid partitioning.…”

Section: Rule Consequent Parametersmentioning

confidence: 99%

Incremental Local Linear Fuzzy Classifier in Fisher Space

Eftekhari

¹

,

Moghaddam

²

,

Forouzanfar

³

et al. 2009

EURASIP J. Adv. Signal Process.

View full text Add to dashboard Cite

Optimizing the antecedent part of neurofuzzy system is an active research topic, for which different approaches have been developed. However, current approaches typically suffer from high computational complexity or lack of ability to extract knowledge from a given set of training data. In this paper, we introduce a novel incremental training algorithm for the class of neurofuzzy systems that are structured based on local linear classifiers. Linear discriminant analysis is utilized to transform the data into a space in which linear discriminancy of training samples is maximized. The neurofuzzy classifier is then built in the transformed space, starting from the simplest form (a global linear classifier). If the overall performance of the classifier was not satisfactory, it would be iteratively refined by incorporating additional local classifiers. In addition, rule consequent parameters are optimized using a local least square approach. Our refinement strategy is motivated by LOLIMOT, which is a greedy partition algorithm for structure training and has been successfully applied in a number of identification problems. The proposed classifier is compared to several benchmark classifiers on a number of well-known datasets. The results prove the efficacy of the proposed classifier in achieving high performance while incurring low computational effort.

show abstract

An Adaptable Gaussian Neuro-Fuzzy Classifier

Cited by 7 publications

References 8 publications

Incremental Local Linear Fuzzy Classifier in Fisher Space

Incremental Local Linear Fuzzy Classifier in Fisher Space

Incremental Local Linear Fuzzy Classifier in Fisher Space

Incremental Local Linear Fuzzy Classifier in Fisher Space

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