A novel neuro-fuzzy method for linguistic feature selection and rule-based classification

Eiamkanitchat, Narissara; Theera-Umpon, Nipon; Auephanwiriyakul, Sansanee

doi:10.1109/iccae.2010.5451487

Cited by 28 publications

(21 citation statements)

References 17 publications

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“…The total average accuracy rate is higher than other high performance Neuro-fuzzy methods [5][6][7], those have been proposed earlier. The ADCNF shown the reduction of complexity and high performance than [7].…”

Section: Resultsmentioning

confidence: 92%

“…The experimental in [4][5][6][7] shown that the neuro-fuzzy system has a good performance for classification. In [5][6] has proposed the neuro-fuzzy model for improving the performance of classification. The method is used three fuzzy membership function for converting the original input to the linguistic value then fed into the neural network.…”

mentioning

confidence: 97%

“…In order to improve the classification accuracy, many researchers have proposed the combination of the neural network and fuzzy system methodology called neuro-fuzzy system. The strength of the two algorithms is selected to use in the neuro-fuzzy system, which is the learning algorithm and the linguistic for rule extraction that is easy to understand [5][6]. The experimental in [4][5][6][7] shown that the neuro-fuzzy system has a good performance for classification.…”

mentioning

confidence: 98%

“…The strength of the two algorithms is selected to use in the neuro-fuzzy system, which is the learning algorithm and the linguistic for rule extraction that is easy to understand [5][6]. The experimental in [4][5][6][7] shown that the neuro-fuzzy system has a good performance for classification. In [5][6] has proposed the neuro-fuzzy model for improving the performance of classification.…”

mentioning

confidence: 98%

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The Adaptive Dynamic Clustering Neuro-Fuzzy System for Classification

Napook

Eiamkanitchat

2015

Lecture Notes in Electrical Engineering

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Abstract. This paper proposes a method of neuro-fuzzy for classification using adaptive dynamic clustering. The method has three parts, the first part is to find the proper number of membership functions by using adaptive dynamic clustering and transform to binary value in a second step. The final step is classification part using neural network. Furthermore the weights from the learning process of the neural network are used as feature eliminates to perform the rule extraction. The experiments used dataset form UCI to verify the proposed methodology. The result shows the high performance of the proposed method.Keywords. Neuro-Fuzzy System, Adaptive Dynamic Clustering, Classification. IntroductionThe neural network is a popular model used for classification. It has been widely used since the strength of robustness for unknown data. Furthermore, the reason of it wildly used is the merit of using the back propagation for learning process. However, the neural network has a weak point of interpreting ability or usually called the black box [1][2][3]. The fuzzy system has been widely used in classification problems the same as the neural network. The fuzzy system can handle the uncertainty and proper for creating the linguistic value. The fuzzy rule base extraction is easy to understand and important for many applications. The weakness of the fuzzy system is no adaptive learning methodology [2]. In order to improve the classification accuracy, many researchers have proposed the combination of the neural network and fuzzy system methodology called neuro-fuzzy system. The strength of the two algorithms is selected to use in the neuro-fuzzy system, which is the learning algorithm and the linguistic for rule extraction that is easy to understand [5][6]. The experimental in [4][5][6][7] shown that the neuro-fuzzy system has a good performance for classification. In [5][6] has proposed the neuro-fuzzy model for improving the performance of classification. The method is used three fuzzy membership function for converting the original input to the linguistic value then fed into the neural network. The method applied back propagation to update neural network and fuzzy membership function. The results from training process are used to generate rule extraction for classification and can use as

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Section: Resultsmentioning

confidence: 92%

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confidence: 97%

mentioning

confidence: 98%

mentioning

confidence: 98%

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The Adaptive Dynamic Clustering Neuro-Fuzzy System for Classification

Napook

Eiamkanitchat

2015

Lecture Notes in Electrical Engineering

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“…The structure of the HANN-L2F is the continuous development of the Neuro-Fuzzy system in [2][3][4][5]. The main component, including neural network and fuzzy system.…”

Section: Introductionmentioning

confidence: 99%

Improvements the HANN-L2F for classification by using k-means

Teyakome

Eiamkanitchat

2015

2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE)

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this paper presents the improved algorithm for the Hybrid Approach of Neural network and Level-2 Fuzzy set (HANN-L2F). The main structure is including 2 parts. The first part is Neuro-Fuzzy system, including the MLP Neural network with the combination of the level-2 Fuzzy system. The second part is using k-nearest neighbor to classify the output from Neuro-fuzzy. The HANN-L2F is an algorithm with high classification performance. However, the process to determine the number of membership functions in HANN-L2F is take time and sometimes results the high number of clusters, this make the high complexity of the overall process. This paper, including the data adjustment in HANN-L2F by using the determination process and implement k-means algorithm to find the appropriate number of clusters. Several types of standard datasets from UCI repository machine learning are used to verify the performance of the propose algorithm. The additional data adjustment process can improve the classification performance of HANN-L2F, furthermore, can reduce the classification time as displayed in the experimental results.

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An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification

Saetern

Eiamkanitchat

2014

Advances in Intelligent Systems and Computing

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A novel neuro-fuzzy method for linguistic feature selection and rule-based classification

Cited by 28 publications

References 17 publications

The Adaptive Dynamic Clustering Neuro-Fuzzy System for Classification

The Adaptive Dynamic Clustering Neuro-Fuzzy System for Classification

Improvements the HANN-L2F for classification by using k-means

An Ensemble K-Nearest Neighbor with Neuro-Fuzzy Method for Classification

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