Evolved fuzzy min-max neural network for new-labeled data classification

Ma, Yanjuan; Liu, Jinhai; Qu, Fuming; Zhu, Hongfei

doi:10.1007/s10489-021-02259-9

Cited by 3 publications

(3 citation statements)

References 28 publications

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“…Each such system should be comprised of main components. The first one is a fuzzifier, which transforms crisp inputs into fuzzy ones; an inference engine that carries out fuzzy reasoning on the fuzzy data to obtain a fuzzy output; the second one is a defuzzifier, which transforms the fuzzy output of the latter component into a crisp format; the third one is a knowledge base, which consists of both a set of FR, and the fourth one is a permissible set of MF known as the database [27]. The sought-for decisions are made by the inference engine utilizing members of the rule base.…”

Section: Proposed Fuzzy Systemsmentioning

confidence: 99%

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

2022

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In our paper, performance of four fuzzy membership function generation methods was studied. These methods were studied in the context of implementing Mamdani fuzzy classification on a set of satellite images for western Iraqi territory. The first method generate triangulate membership functions using mean, minimum (min) and maximum (max) of histogram attribute values (AV), while peak and standard deviation (STD) of these AV were used in the second. On the other hand, in the third and fourth methods, Gaussian membership functions are generated using same mentioned values in the first and second method respectively. The goal was to generate a Mamdani type fuzzy inference system the membership function (MF) of each fuzzy set and implementing the AV of western Iraqi territory training data sets. A pixel-by-pixel comparison of each method with traditional maximum likelihood method (ML) was made on data sets comprising six bands of satellite imagery of the western Iraqi region taken by the Landsat-5 satellite. Simulation results of these performance comparisons singled out that the method using Gaussian MFs together with peak and STD of the AV as the best achiever with a similarity of 83.16 percent for band (3) of the studied area.

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Section: Proposed Fuzzy Systemsmentioning

confidence: 99%

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

2022

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“…Fuzzy min-max neural networks are used to make the task easier and these implements fuzzy sets to accomplish tasks. 27,28…”

Section: Introductionmentioning

confidence: 99%

“…Fuzzy min-max neural networks are used to make the task easier and these implements fuzzy sets to accomplish tasks. 27,28 Potential drawbacks of using machine learning and deep learning models for sleep stage classification include:…”

Section: Introductionmentioning

confidence: 99%

An evolutionary model for sleep quality analytics using fuzzy system

Hangaragi,

Nizampatnam,

Kaliyaperumal

et al. 2023

Proc Inst Mech Eng H

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Electroencephalography (EEG) is a neuro signal reflecting brain activity. These signals provide information about brain activity, eye movements, and muscle tone, which can be used to determine the sleep stage. Categorizing sleep stages can be done manually by visually. Alternatively, automated algorithms can be developed using machine learning techniques to classify sleep stages based on signal features and patterns. This paper aims to automatically classify sleep stages based on extracted patterns from EEG signals. A fuzzy min-max neural network is proposed and implemented for sleep stage classification and clustering. The paper concludes that the fuzzy min-max neural network outperforms other tested methods in sleep stage classification. The models implemented in the study include K-Nearest Neighbor (KNN), Random Forest, Decision Tree, XGBoost, AdaBoost, Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Convolutional Neural Network (CNN), and the fuzzy min-max classifier. The results indicate that the fuzzy classifier achieves the highest accuracy of 86%, followed by the CNN model with 81%. Among the machine learning algorithms, Random Forest with an accuracy of 55.46%, followed by XGBoost with 53.18%, surpassing the other algorithms used in the experiment. AdaBoost and Gaussian Naive Bayes both achieve an accuracy of 45.10%. Decision Tree, KNN, LDA, and QDA yield accuracies of 37.66%, 16.46%, 28.57%, and 29.5% respectively. These findings demonstrate the efficiency of the fuzzy min-max neural network and the superiority of the fuzzy classifier and CNN models in sleep stage classification, indicating their potential for accurate automated sleep stage analysis.

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Fuzzy min–max neural networks: a bibliometric and social network analysis

Kenger

Özceylan

2023

Neural Comput & Applic

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Evolved fuzzy min-max neural network for new-labeled data classification

Cited by 3 publications

References 28 publications

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

A comparative study of multiband mamdani fuzzy classification methods for west of Iraq satellite image

An evolutionary model for sleep quality analytics using fuzzy system

Fuzzy min–max neural networks: a bibliometric and social network analysis

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