A neuro-fuzzy method to learn fuzzy classification rules from data

Nauck, Detlef; Kruse, Rudolf

doi:10.1016/s0165-0114(97)00009-2

Cited by 333 publications

(155 citation statements)

References 11 publications

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“…As far as the classification rate and the number of rules are concerned, our method converges to a network of just three rules, no more than the classes of the problem, while not loosing in performance when compared to networks with larger numbers of hidden nodes. In addition to the experiments presented herein, our method outperforms others in the literature as well (7 rules, 96.7% [5]) (17 rules, 95.3% [6]) (9 rules, 95.3% [7]) (7 rules, 96% [8]). …”

Section: Resultssupporting

confidence: 60%

Improving the Performance of Resource Allocation Networks through Hierarchical Clustering of High-Dimensional Data

Tsapatsoulis

Wallace

Kasderidis

2003

Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003

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Abstract. Adaptivity to non-stationary contexts is a very important property for intelligent systems in general, as well as to a variety of applications of knowledge based systems in era of "ambient intelligence". In this paper we present a modified Resource Allocating Network architecture that allows for online adaptation and knowledge modelling through its adaptive structure. As in any neural network system proper parameter initialization reduces training time and effort. However, in RAN architectures, proper parameter initialization also leads to compact modelling (less hidden nodes) of the process under examination, and consequently to better generalization. In the cases of high-dimensional data parameter initialization is both difficult and time consuming. In the proposed scheme a high -dimensional, unsupervised clustering method is used to properly initialize the RAN architecture. Clusters correspond to the initial nodes of RAN, while output layer weights are also extracted from the clustering procedure. The efficiency of the proposed method has been tested on several classes of publicly available data (iris, ionosphere, etc.)

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Section: Resultssupporting

confidence: 60%

Improving the Performance of Resource Allocation Networks through Hierarchical Clustering of High-Dimensional Data

Tsapatsoulis

Wallace

Kasderidis

2003

Artificial Neural Networks and Neural Information Processing — ICANN/ICONIP 2003

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“…If the system does not perform as expected, it is straightforward to adapt the rules until the desired behavior is obtained, while many other methods crucially depend on the availability of good training data to arrive at 'blackbox' models. Moreover, if such training data is actually available, the rules that have manually been constructed can be refined in an automated way [70]. A similar use of fuzzy rules is made in [90] with the aim of clustering web search results.…”

Section: Manipulation Of Search Resultsmentioning

confidence: 99%

Fuzzy Methods on the Web: A Critical Discussion

Schockaert

Makarytska

Cock

2010

35 Years of Fuzzy Set Theory

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Gradual concepts abound in many web-related domains, ranging from the notion of relevance in information retrieval, to the strength of connection in online social networks. As a result, fuzzy set theory is often a natural choice in implementing web systems. In this chapter, we give an overview of applications of fuzzy set theory in this area, focusing in particular on information retrieval, the semantic web, and recommender systems. In each case, we contrast fuzzy methods with other approaches, analyzing why and how the ideas of fuzzy set theory may be beneficial.

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“…If a rule with an identical antecedent does not exist in the rule base, then this rule will be created. After all the patterns have been processed, the rule base will be complete 24 .…”

Section: Learning Modelsmentioning

confidence: 99%

A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations

Carvalho

Nassar

Azevedo

et al. 2008

Arq. Neuro-Psiquiatr.

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-Objective: To investigate different fuzzy arithmetical operations to support in the diagnostic of epileptic events and non epileptic events. Method: A neuro-fuzzy system was developed using the NEFCLASS (NEuro Fuzzy CLASSIfication) architecture and an artificial neural network with backpropagation learning algorithm (ANNB). Results: The study was composed by 244 patients with a bigger frequency of the feminine sex. The number of right decisions at the test phase, obtained by the NEFCLASS and ANNB was 83.60% and 90.16%, respectively. The best sensibility result was attained by NEFCLASS (84.90%); the best specificity result were attained by ANNB with 95.65%. Conclusion: The proposed neuro-fuzzy system combined the artificial neural network capabilities in the pattern classifications together with the fuzzy logic qualitative approach, leading to a bigger rate of system success.KEY WORDS: epileptic events, non epileptic events, artificial neural network, fuzzy logic. Um sistema neuro-difuso para auxiliar no diagnóstico de eventos epilépticos e eventos não epilépticos utilizando diferentes operações aritméticas difusasResumo -Objetivo: Investigar diferentes operações aritméticas difusas para auxíliar no diagnóstico de eventos epilépticos e eventos não-epilépticos. Método: Um sistema neuro-difuso foi desenvolvido utilizando a arquitetura NEFCLASS (NEuro Fuzzy CLASSIfication) e uma rede neural artificial com o algoritmo de aprendizagem backpropagation (RNAB). Resultados: A amostra estudada foi de 244 pacientes com maior freqüência no sexo feminino. O número de decisões corretas na fase de teste, obtidas através do NEFCLASS e RNAB foi de 83,60% e 90,16%, respectivamente. O melhor resultado de sensibilidade foi obtido com o NEFCLASS (84,90%); o melhor resultado de especificidade foi obtido com a RNAB (95,65%). Conclusão: O sistema neuro-difuso proposto combinou a capacidade das redes neurais artificiais na classificação de padrões juntamente com a abordagem qualitativa da logica difusa, levando a maior taxa de acertos do sistema. PALAVRAS-CHAVE: eventos epilépticos, eventos não epilépticos, rede neural artificial, lógica difusa.

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A neuro-fuzzy method to learn fuzzy classification rules from data

Cited by 333 publications

References 11 publications

Improving the Performance of Resource Allocation Networks through Hierarchical Clustering of High-Dimensional Data

Improving the Performance of Resource Allocation Networks through Hierarchical Clustering of High-Dimensional Data

Fuzzy Methods on the Web: A Critical Discussion

A neuro-fuzzy system to support in the diagnostic of epileptic events and non-epileptic events using different fuzzy arithmetical operations

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