Subtractive Clustering and Particle Swarm Optimization Based Fuzzy Classifier

2019 ◽  
Vol 8 (3) ◽  
pp. 108-122 ◽  
Author(s):  
Halima Salah ◽  
Mohamed Nemissi ◽  
Hamid Seridi ◽  
Herman Akdag
Keyword(s):  
Particle Swarm Optimization ◽  
State Of The Art ◽  
Particle Swarm ◽  
Main Idea ◽  
Fuzzy Rule ◽  
Rule Base ◽  
Subtractive Clustering ◽  
Data Sets ◽  
Fuzzy Classifier ◽  
Swarm Optimization

Setting a compact and accurate rule base constitutes the principal objective in designing fuzzy rule-based classifiers. In this regard, the authors propose a designing scheme based on the combination of the subtractive clustering (SC) and the particle swarm optimization (PSO). The main idea relies on the application of the SC on each class separately and with a different radius in order to generate regions that are more accurate, and to represent each region by a fuzzy rule. However, the number of rules is then affected by the radiuses, which are the main preset parameters of the SC. The PSO is therefore used to define the optimal radiuses. To get good compromise accuracy-compactness, the authors propose using a multi-objective function for the PSO. The performances of the proposed method are tested on well-known data sets and compared with several state-of-the-art methods.

scholarly journals Construction and Optimization of Fuzzy Rule-Based Classifier with a Swarm Intelligent Algorithm

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Li Mao ◽  
Qidong Chen ◽  
Jun Sun
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Rule ◽  
Fuzzy Rules ◽  
Fuzzy Classification ◽  
Rule Base ◽  
Classification Problems ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Rule Based

In this paper, we propose a particle swarm optimization method incorporating quantum qubit operation to construct and optimize fuzzy rule-based classifiers. The proposed algorithm, denoted as QiQPSO, is inspired by the quantum computing principles. It employs quantum rotation gates to update the probability of each qubit with the corresponding quantum angle updating according to the update equation of the quantum-behaved particle swarm optimization (QPSO). After description of the principle of QiQPSO, we show how to apply QiQPSO to establish a fuzzy classifier through two procedures. The QiQPSO algorithm is first used to construct the initial fuzzy classification system based on the sample data and the grid method of partitioning the feature space, and then the fuzzy rule base of the initial fuzzy classifier is optimized further by QiQPSO in order to reduce the number of the fuzzy rules and thus improve its interpretability. In order to verify the effectiveness of the proposed method, QiQPSO is tested on various real-world classification problems. The experimental results show that the QiQPSO is able to effectively select feature variables and fuzzy rules of the fuzzy classifiers with high classification accuracies. The performance comparison with other methods also shows that the fuzzy classifier optimized by QiQPSO has higher interpretability as well as comparable or even better classification accuracies.

scholarly journals Data and Feature Reduction in Fuzzy Modeling through Particle Swarm Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-21 ◽  
Author(s):  
S. Sakinah S. Ahmad ◽  
Witold Pedrycz
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Fuzzy Model ◽  
Fuzzy Rule ◽  
Search Space ◽  
Fuzzy Modeling ◽  
Optimization Techniques ◽  
Feature Reduction ◽  
Data Sets ◽  
Swarm Optimization

The study is concerned with data and feature reduction in fuzzy modeling. As these reduction activities are advantageous to fuzzy models in terms of both the effectiveness of their construction and the interpretation of the resulting models, their realization deserves particular attention. The formation of a subset of meaningful features and a subset of essential instances is discussed in the context of fuzzy-rule-based models. In contrast to the existing studies, which are focused predominantly on feature selection (namely, a reduction of the input space), a position advocated here is that a reduction has to involve both data and features to become efficient to the design of fuzzy model. The reduction problem is combinatorial in its nature and, as such, calls for the use of advanced optimization techniques. In this study, we use a technique of particle swarm optimization (PSO) as an optimization vehicle of forming a subset of features and data (instances) to design a fuzzy model. Given the dimensionality of the problem (as the search space involves both features and instances), we discuss a cooperative version of the PSO along with a clustering mechanism of forming a partition of the overall search space. Finally, a series of numeric experiments using several machine learning data sets is presented.

scholarly journals A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification

2021 ◽  
Author(s):  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
GG Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Image Classification ◽  
State Of The Art ◽  
Particle Swarm ◽  
Image Data ◽  
The State ◽  
Data Sets ◽  
Deep Convolutional Neural Networks ◽  
Swarm Optimization ◽  
Convolutional Autoencoder

© 2012 IEEE. Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data during the past several years. However, they are unable to construct the state-of-the-art CNNs due to their intrinsic architectures. In this regard, we propose a flexible CAE (FCAE) by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional CAE. We also design an architecture discovery method by exploiting particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed FCAE with much less computational resource and without any manual intervention. We test the proposed approach on four extensively used image classification data sets. Experimental results show that our proposed approach in this paper significantly outperforms the peer competitors including the state-of-the-art algorithms. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

scholarly journals A Particle Swarm Optimization-Based Flexible Convolutional Autoencoder for Image Classification

2021 ◽  
Author(s):  
Y Sun ◽  
Bing Xue ◽  
Mengjie Zhang ◽  
GG Yen
Keyword(s):  
Particle Swarm Optimization ◽  
Image Classification ◽  
State Of The Art ◽  
Particle Swarm ◽  
Image Data ◽  
The State ◽  
Data Sets ◽  
Deep Convolutional Neural Networks ◽  
Swarm Optimization ◽  
Convolutional Autoencoder

© 2012 IEEE. Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking to deep convolutional neural networks (CNNs) for classifying image data during the past several years. However, they are unable to construct the state-of-the-art CNNs due to their intrinsic architectures. In this regard, we propose a flexible CAE (FCAE) by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional CAE. We also design an architecture discovery method by exploiting particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed FCAE with much less computational resource and without any manual intervention. We test the proposed approach on four extensively used image classification data sets. Experimental results show that our proposed approach in this paper significantly outperforms the peer competitors including the state-of-the-art algorithms. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

FORMATION OF FUZZY IF-THEN RULES AND MEMBERSHIP FUNCTION USING ENHANCED PARTICLE SWARM OPTIMIZATION

2013 ◽  
Vol 21 (01) ◽  
pp. 103-126 ◽  
Author(s):  
P. GANESHKUMAR ◽  
C. RANI ◽  
S. N. DEEPA
Keyword(s):  
Particle Swarm Optimization ◽  
Membership Function ◽  
Particle Swarm ◽  
Bench Mark ◽  
Data Sets ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Rule Set ◽  
Enhanced Particle Swarm Optimization ◽  
Test Result

This paper proposes an Enhanced Particle Swarm Optimization (EPSO) for extracting optimal rule set and tuning membership function for fuzzy logic based classifier model. The standard PSO is more sensitive to premature convergence due to lack of diversity in the swarm and can easily get trapped into local minima when it is used for data classification. To overcome this issue, BLX-α crossover and Non-uniform mutation from Genetic Algorithm (GA) are incorporated in addition to standard velocity and position updating of PSO. The performance of the proposed approach is evaluated using ten publicly available bench mark data sets. From the simulation study, it is found that the proposed approach enhances the convergence and generates a comprehensible fuzzy classifier system with high classification accuracy for all the data sets. Statistical analysis of the test result shows the suitability of the proposed method over other approaches reported in the literature.

scholarly journals A Review of Geophysical Modeling Based on Particle Swarm Optimization

2021 ◽  
Author(s):  
Francesca Pace ◽  
Alessandro Santilano ◽  
Alberto Godio
Keyword(s):  
Particle Swarm Optimization ◽  
Inverse Modeling ◽  
Critical Evaluation ◽  
Particle Swarm ◽  
Geophysical Data ◽  
Magnetic Data ◽  
Data Sets ◽  
Swarm Optimization ◽  
Geophysical Modeling ◽  
Original Works

AbstractThis paper reviews the application of the algorithm particle swarm optimization (PSO) to perform stochastic inverse modeling of geophysical data. The main features of PSO are summarized, and the most important contributions in several geophysical fields are analyzed. The aim is to indicate the fundamental steps of the evolution of PSO methodologies that have been adopted to model the Earth’s subsurface and then to undertake a critical evaluation of their benefits and limitations. Original works have been selected from the existing geophysical literature to illustrate successful PSO applied to the interpretation of electromagnetic (magnetotelluric and time-domain) data, gravimetric and magnetic data, self-potential, direct current and seismic data. These case studies are critically described and compared. In addition, joint optimization of multiple geophysical data sets by means of multi-objective PSO is presented to highlight the advantage of using a single solver that deploys Pareto optimality to handle different data sets without conflicting solutions. Finally, we propose best practices for the implementation of a customized algorithm from scratch to perform stochastic inverse modeling of any kind of geophysical data sets for the benefit of PSO practitioners or inexperienced researchers.

scholarly journals A New Opinion Mining Method based on Fuzzy Classifier and Particle Swarm Optimization (PSO) Algorithm

2018 ◽  
Vol 18 (2) ◽  
pp. 36-50
Author(s):  
Samira Bordbar ◽  
Pirooz Shamsinejad
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Opinion Mining ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Fuzzy Rules ◽  
Training Data ◽  
Fuzzy Classifier ◽  
Swarm Optimization ◽  
Main Challenge

Abstract Opinion Mining or Sentiment Analysis is the task of extracting people final opinion about something through their unstructured sentiments. The Opinion Mining process is as follows: first, product features which are most important to a user are extracted from his/her comments. Then, sentiments will be emotionally classified using their emotional implications. In this paper we propose an opinion classification method based on Fuzzy Logic. Up to now, a few methods have taken advantage of fuzzy logic in opinion classification and all of them have imported fuzzy rules into system as background knowledge. But the main challenge here is finding the fuzzy rules. Our contribution is to automatically extract fuzzy rules and their parameters from training data. Here we have used the Particle Swarm Optimization (PSO) algorithm to extract fuzzy rules from training data. Also, for better results we have devised a mutation-based PSO. All proposed methods have been implemented and tested on relevant data. Results confirm that our method can reach better accuracy than current state of the art methods in this domain.

Knowledge Acquisition in Fuzzy-Rule-Based Systems With Particle-Swarm Optimization

2010 ◽  
Vol 18 (6) ◽  
pp. 1083-1097 ◽  
Author(s):  
R. P. Prado ◽  
S. Garcia-Galan ◽  
J. E. Munoz Exposito ◽  
A. J. Yuste
Keyword(s):  
Particle Swarm Optimization ◽  
Knowledge Acquisition ◽  
Particle Swarm ◽  
Fuzzy Rule ◽  
Swarm Optimization ◽  
Rule Based ◽  
Rule Based Systems
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