A Neuro-Fuzzy Model for Dimensionality Reduction and Its Application

Kolodyazhniy, Vitaliy; Klawonn, Frank; Tschumitschew, Katharina

doi:10.1142/s0218488507004881

Cited by 7 publications

(4 citation statements)

References 14 publications

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“…ey used Lyapunov exponents for feature extraction, and ANFIS is used for classification. Kolodyazhniy et al [55] used PCA for dimension reduction and NF Kolmogorov's network for classification for waste water treatment plant data. Schclar et al [56] ensemble various models based on the dimensionality reduction.…”

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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“…and nh tuned synaptic weights [2] ilj w . In total, the autoencoder contains 2nmh tuned synaptic weights and ( ) n m h + membership functions that is significantly less than in the systems, which were described in [14][15][16][17].…”

Section: Autoencoder Based On Gnfnsmentioning

confidence: 99%

“…In distinction from autoencoders, which are described in [14][15][16][17] the proposed system contains less number of the membership functions (it reduces its computational implementation) and has a high speed of learning algorithm due to the optimized choice of learning rate parameters.…”

Section: The Learning Of the Gnfn Autoencodermentioning

confidence: 99%

“…Such network has the two layers for information processing and is characterized by the universal approximation properties according to A. Kolmogorov -V. Arnold theorem. Using these networks, the authors in [14] have proposed the neuro-fuzzy model for the dimensionality reduction, which is learned using error backpropagation algorithm with gradient methods. In [15][16][17] the optimized learning algorithms for two-layer autoencoders based on the neo-fuzzy neurons were proposed, which allow significantly reducing the learning time.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

The Autoencoder Based on Generalized Neo-Fuzzy Neuron and its Fast Learning for Deep Neural Networks

Bodyanskiy

Peleshko

Rashkevych

et al. 2018

2018 IEEE Second International Conference on Data Stream Mining &Amp; Processing (DSMP)

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In this paper the autoencoder based on the generalized neo-fuzzy neurons is proposed. Also its fast learning algorithm based on quadratic criterion was proposed. Such system can be used as part of deep learning systems. The proposed autoencoder is characterized by high learning speed and less number of tuned parameters in comparison with wellknown autoencoders of "bottle neck" type. The efficiency of proposed approach has been justified based on different benchmarks and real data sets.

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