Fuzzy sets in pattern recognition: methodology and methods
Pattern Recognition
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Pattern Classification: Neuro-Fuzzy Methods and Their Comparison
Introduction to Artificial Neural Systems
Introduction to Artificial Neural Systems
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing
Segmentation of multispectral remote sensing images using active support vector machines
Pattern Recognition Letters
Artificial Neural Networks for Document Analysis and Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Remote Sensing Digital Image Analysis: An Introduction
Remote Sensing Digital Image Analysis: An Introduction
A novel fuzzy classifier based on product aggregation operator
Pattern Recognition
Neural Network Learning: Theoretical Foundations
Neural Network Learning: Theoretical Foundations
Incorporating Fuzzy Membership Functions into the Perceptron Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks for classification: a survey
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
An adaptive neuro-fuzzy system for automatic image segmentation and edge detection
IEEE Transactions on Fuzzy Systems
Self-organization for object extraction using a multilayer neural network and fuzziness measures
IEEE Transactions on Fuzzy Systems
Learning in linear neural networks: a survey
IEEE Transactions on Neural Networks
A hybrid method for MRI brain image classification
Expert Systems with Applications: An International Journal
A fuzzy intelligent approach to the classification problem in gene expression data analysis
Knowledge-Based Systems
Title Natural computing: A problem solving paradigm with granular information processing
Applied Soft Computing
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A new model for neuro-fuzzy (NF) classification systems is proposed. The motivation is to utilize the feature-wise degree of belonging of patterns to all classes that are obtained through a fuzzification process. A fuzzification process generates a membership matrix having total number of elements equal to the product of the number of features and classes present in the data set. These matrix elements are the input to neural networks. The effectiveness of the proposed model is established with four benchmark data sets (completely labeled) and two remote sensing images (partially labeled). Different performance measures such as misclassification, classification accuracy and kappa index of agreement for completely labeled data sets, and @b index of homogeneity and Davies-Bouldin (DB) index of compactness for remotely sensed images are used for quantitative analysis of results. All these measures supported the superiority of the proposed NF classification model. The proposed model learns well even with a lower percentage of training data that makes the system fast.