A Hybrid Neuro-Fuzzy and Feature Reduction Model for Classification

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.

Modeling human thinking about similarities by neuromatrices in the perspective of fuzzy logic

“…For the example presented in (20), the outer product decomposition takes the following form: The original data from [54] were rounded and rescaled such that the minimal value equals one, whereas the maximal amounts to eleven. Colors in the second column were generated by Spectra software, which converts the wavelengths to the RGB color system [118].…”

“…Currently, it is still common to come across various versions of this method in scientific papers. For example, Zhu et al [19] combined PCA with linear hashing and manifold learning for similarity search in color images, while Das et al [20] used PCA to remove irrelevant features in their hybrid neuro-fuzzy reduction model for classification purposes. Other contemporary advancements in regarding PCA may be found, for example, in [21][22][23].…”

Optimized hybrid investigative based dimensionality reduction methods for malaria vector using KNN classifier

“…A hybridized neuro-fuzzy with feature reduction approach was proposed for classification of data analysis, for dealing with uncertainty issues. Their result showed a considerable improvement in terms of accuracy and elimination of redundancy of information, and proposed solving real life gene expression classification problems [28].…”

Intelligent Neutrosophic Diagnostic System for Cardiotocography Data

“…Das et al [ 28 ] simulated a hybrid neurofuzzy and feature reduction (NF-FR) model to analyse data. The proposed NF-FR model uses a feature-based class pertinence fuzzification process for all patterns.…”