Multi-Level Fuzzy Min-Max Neural Network Classifier

Author

Davtalab, Reza ; Dezfoulian, Mir Hossein ; Mansoorizadeh, Muharram

Author_Institution

Bu-Ali Sina Univ., Hamedan, Iran

Volume

25

Issue

3

fYear

2014

fDate

Mar-14

Firstpage

470

Lastpage

482

Abstract

In this paper a multi-level fuzzy min-max neural network classifier (MLF), which is a supervised learning method, is described. MLF uses basic concepts of the fuzzy min-max (FMM) method in a multi-level structure to classify patterns. This method uses separate classifiers with smaller hyperboxes in different levels to classify the samples that are located in overlapping regions. The final output of the network is formed by combining the outputs of these classifiers. MLF is capable of learning nonlinear boundaries with a single pass through the data. According to the obtained results, the MLF method, compared to the other FMM networks, has the highest performance and the lowest sensitivity to maximum size of the hyperbox parameter (θ), with a training accuracy of 100% in most cases.

Keywords

fuzzy neural nets; learning (artificial intelligence); minimax techniques; pattern classification; FMM method; MLF; hyperboxes; multilevel fuzzy min-max neural network classifier; multilevel structure; pattern classification; supervised learning method; Accuracy; Biological neural networks; Indexes; Learning systems; Neurons; Training; Classification; fuzzy min-max; hyperbox; machine learning; neural networks; neurofuzzy; neuron; supervised learning;

fLanguage

English

Journal_Title

Neural Networks and Learning Systems, IEEE Transactions on

Publisher

ieee

ISSN

2162-237X

Type

jour

DOI

10.1109/TNNLS.2013.2275937

Filename

6582672