A neuro-fuzzy classification technique using dynamic clustering and GSS rule generation

Singh, Heisnam Rohen; Biswas, Saroj; Purkayastha, Biswajit

doi:10.1016/j.cam.2016.04.023

Cited by 14 publications

(8 citation statements)

References 19 publications

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“…It filters the high-quality summary sentences on the document understanding conference data corpus. Singh et al [46] proposed an enhanced NF model used for clustering that reduces the number of linguistic variables as compared to the NF model. Nilashi et al [47] used ensembles ANFIS model, clustering along with dimensionality reduction for prediction of hepatitis disease diagnosis.…”

Section: Literature Surveymentioning

confidence: 99%

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Das

Naik

Behera

2020

Advances in Fuzzy Systems

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The evolvement of the fuzzy system has shown influential and successful in many universal approximation capabilities and applications. This paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. This proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. During the fuzzification process, all the features are expanded based on the number of classes available in the dataset. It helps to deal with the uncertainty issues and assists the Artificial Neural Network- (ANN-) based model to achieve better performance. However, the complexity of the problem increases due to this expansion of input features in the fuzzification process. These expanded features may not always contribute significantly to the model. To overcome this problem, feature reduction (FR) is used to filter out the insignificant features, resulting the network less computational cost. These reduced significant features are used in the ANN-based model to classify the data. The effectiveness of this proposed model is tested and validated with ten benchmark datasets (both balanced and unbalanced) to demonstrate the performance of the proposed NF-FR model. The performance comparison of the NF-FR model with other counterparts has been carried out based on various performance measures such as classification accuracy, root means square error, precision, recall, and f-measure for quantitative analysis of the results. The obtained simulated results have been tested using the Friedman, Holm, and ANOVA tests under the null hypothesis for statistical validity and correctness proof of the results. The result analysis and statistical analysis show that the NF-FR model has achieved a considerable improvement in accuracy and is found to be efficient in eliminating redundant and noisy information.

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Section: Literature Surveymentioning

confidence: 99%

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

Das

Naik

Behera

2020

Advances in Fuzzy Systems

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“…Alkhraijah and Abido [14] classified various disturbance event types by introducing Adaptive Neuro Fuzzy Inference System. Power disturbances five important features are extracted by using wavelet transform and Fourier Transform in this method.…”

Section: Related Workmentioning

confidence: 99%

A Critical Review on Medical Image Processing Techniques

2020

jcr

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Medical image processing is highly a vital task in the field of health informatics. Various techniques are used to process the images captured using methods like Medical Resonance Imaging (MRI), Computed Tomography(CT), Positron Emission Tomography (PET) and the process includes, denoising, feature extraction, segmentation and classification. Different methods are implemented for every process. Process of eliminating noise from an acquired image is termed as denoising and various filters are designed for the same. Different features like local binary pattern, gray level similarity are extracted for segmentation process. Clustering algorithms like fuzzy-c-means, fuzzy-k-means algorithms are used for image segmentation. Particle Swarm Optimization (PSO), convolutional neural networks are also used for the purpose of image segmentation. Performance of these methods are compared using various performance evaluation metrics like, precision, recall, accuracy, sensitivity.

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“…A variety of system structures and learning algorithms are available for neuro-fuzzy methods [9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25][26]. Learning of the classical neuro-fuzzy systems is based on the gradient descent method [9].…”

Section: Introductionmentioning

confidence: 99%

“…An adaptive neuro-fuzzy system for building and optimizing fuzzy models has been proposed [14]. A variety of neuro-fuzzy methods are also proposed recently [15][16][17][18][19][20][21]. The applications of neurofuzzy methods include feature selection [19,21], classification [15][16][17][18][19][20], and image processing [17].…”

Section: Introductionmentioning

confidence: 99%

“…A variety of neuro-fuzzy methods are also proposed recently [15][16][17][18][19][20][21]. The applications of neurofuzzy methods include feature selection [19,21], classification [15][16][17][18][19][20], and image processing [17]. Furthermore, neuro-fuzzy methods using complex-valued inputs and outputs have been proposed and applied to image processing and time-series prediction [22,23].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

Hata¹,

Akhand²,

Islam³

et al. 2018

IJISA

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Abstract-The conventional real-valued neuro-fuzzy method (RNF) is based on classic fuzzy systems with antecedent membership functions and consequent singletons. Rules in RNF are made by all the combinations of membership functions; thus, the number of rules as well as total parameters increase rapidly with the number of inputs. Although network parameters are relatively less in the recently developed complex-valued neuro-fuzzy (CVNF) and quaternion neuro-fuzzy (QNF), parameters increase with number of inputs. This study investigates simplified fuzzy rules that constrain rapid increment of rules with inputs; and proposed simplified RNF (SRNF), simplified CVNF (SCVNF) and simplified QNF (SQNF) employing the proposed simplified fuzzy rules in conventional methods. The proposed simplified neuro-fuzzy learning methods differ from the conventional methods in their fuzzy rule structures. The methods tune fuzzy rules based on the gradient descent method. The number of rules in these methods are equal to the number of divisions of input space; and hence they require significantly less number of parameters to be tuned. The proposed methods are tested on function approximations and classification problems. They exhibit much less execution time than the conventional counterparts with equivalent accuracy. Due to less number of parameters, the proposed methods can be utilized for the problems (e.g., real-time control of large systems) where the conventional methods are difficult to apply due to time constrain.

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A neuro-fuzzy classification technique using dynamic clustering and GSS rule generation

Cited by 14 publications

References 19 publications

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

A Critical Review on Medical Image Processing Techniques

Simplified Real-, Complex-, and Quaternion-Valued Neuro-Fuzzy Learning Algorithms

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