Abstract
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.
Highlights
In the last few decades, machine learning [1] is a key research area among the researchers due to the dynamic generation and availability of large volume data
Our present investigation of the proposed Neuro-Fuzzy and Feature Reduction (NF-feature reduction (FR)) model is justified with class belongingness fuzzification of input features. ese fuzzified features are filtered out by the Principal Component Analysis (PCA) to produce the reduced features. ese reduced features are passed to the Artificial Neural Network- (ANN-)BPN based model for training and testing. is experimentation is done with ten numbers of both balanced and unbalanced datasets
We have tested under analysis of variance (ANOVA), Tukey, and Dunnett tests, and the performance of our proposed model was found to be significantly better as compared to other models
Summary
Received 15 September 2019; Revised 18 November 2019; Accepted 16 January 2020; Published 1 March 2020. Is paper proposes a hybrid Neuro-Fuzzy and Feature Reduction (NF-FR) model for data analysis. Is proposed NF-FR model uses a feature-based class belongingness fuzzification process for all the patterns. 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. Ese 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. Feature reduction (FR) is used to filter out the insignificant features, resulting the network less computational cost. ese reduced significant features are used in the ANN-based model to classify the data. e 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. e 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. e 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. e 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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