2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)最新文献_第2页

Analysis of the impact of using different diversity functions for the subgroup discovery algorithm NMEEF-SD 不同多样性函数对子群发现算法NMEEF-SD的影响分析

2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) Pub Date : 2011-04-11 DOI: 10.1109/GEFS.2011.5949498

C. J. Carmona, P. González, M. J. Jesús, F. Herrera

引用次数: 1

A Fast Iterative Rule-based Linguistic Classifier for hyperspectral remote sensing tasks 基于快速迭代规则的高光谱遥感语言分类器

2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) Pub Date : 2011-04-11 DOI: 10.1109/GEFS.2011.5949501

D. Stavrakoudis, G. Galidaki, I. Gitas, Ioannis B. Theocharis

引用次数: 3

Implementation of Fuzzy NARX IMC PID control of PAM robot arm using Modified Genetic Algorithms 应用改进遗传算法实现PAM机械臂模糊NARX IMC PID控制

2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) Pub Date : 2011-04-11 DOI: 10.1109/GEFS.2011.5949491

H. Anh

引用次数: 3

Iterative Rule Learning of Quantified Fuzzy Rules for control in mobile robotics 移动机器人控制量化模糊规则的迭代规则学习

2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) Pub Date : 2011-04-11 DOI: 10.1109/GEFS.2011.5949500

Ismael Rodríguez-Fdez, M. Mucientes, Alberto Bugarín-Diz

引用次数: 2

KASIA approach vs. Differential Evolution in Fuzzy Rule-Based meta-schedulers for Grid computing 网格计算中基于模糊规则的元调度器的KASIA方法与差分进化

2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) Pub Date : 2011-04-11 DOI: 10.1109/GEFS.2011.5949488

R. P. Prado, S. G. Galán, J. E. M. Expósito

引用次数: 5

A discussion on the accuracy-complexity relationship in modelling fish habitat preference using genetic Takagi-Sugeno fuzzy systems 用遗传Takagi-Sugeno模糊系统建模鱼类生境偏好的精度-复杂性关系探讨

2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) Pub Date : 2011-04-11 DOI: 10.1109/GEFS.2011.5949490

S. Fukuda, Jun Nakajima, B. Baets, W. Waegeman, T. Mukai, A. Mouton, N. Onikura

{"title":"A discussion on the accuracy-complexity relationship in modelling fish habitat preference using genetic Takagi-Sugeno fuzzy systems","authors":"S. Fukuda, Jun Nakajima, B. Baets, W. Waegeman, T. Mukai, A. Mouton, N. Onikura","doi":"10.1109/GEFS.2011.5949490","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949490","url":null,"abstract":"The relationship among accuracy, interpretability, and complexity of genetic fuzzy systems (GFSs) is a hot topic and is actively studied in the GFS domain. Because different problems have different views of interpretation, it is quite difficult to evaluate the interpretability of GFSs in general. The present study aims to analyze accuracy-complexity relationship in fish habitat modelling using a genetic Takagi-Sugeno fuzzy model called fuzzy habitat preference model (FHPM). The model complexity was defined by bit lengths of a genetic algorithm (GA) assigned to the consequent part of the model, while fuzzy rules and antecedent parts were kept the same. FHPM was developed on the basis of the mean squared errors between the composite habitat preference and the observed presence-absence of fish. The model accuracy was evaluated using multiple performance measures. As a result, the different model complexities resulted in slightly different habitat preference curves and model accuracies. At some complexities, the model accuracy was found to be slightly improved with increased model complexity. The result suggests that an optimal point exists where the model complexity can take a balance between the accuracy and the complexity of the target models, which depends partly on data characteristics and model formulations of the GFSs.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123599684","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 4

Double cross-validation for performance evaluation of multi-objective genetic fuzzy systems 多目标遗传模糊系统性能评价的双交叉验证

2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) Pub Date : 2011-04-11 DOI: 10.1109/GEFS.2011.5949503

H. Ishibuchi, Yusuke Nakashima, Y. Nojima

{"title":"Double cross-validation for performance evaluation of multi-objective genetic fuzzy systems","authors":"H. Ishibuchi, Yusuke Nakashima, Y. Nojima","doi":"10.1109/GEFS.2011.5949503","DOIUrl":"https://doi.org/10.1109/GEFS.2011.5949503","url":null,"abstract":"We propose an idea of using repeated double cross-validation to evaluate the generalization ability of multi-objective genetic fuzzy systems (MoGFS). The main advantage of MoGFS approaches is that a large number of non-dominated fuzzy rule-based systems are obtained by their single run. Each of the obtained fuzzy rule-based systems has a different tradeoff with respect to conflicting objectives such as accuracy and complexity. One controversial issue in the MoGFS field (and also in the field of multi-objective optimization in general) is how to choose the final solution from the obtained non-dominated ones. Since this selection is supposed to be done by human users, it is very difficult to rigorously discuss the generalization ability of the finally-selected fuzzy rule-based system. To tackle this difficulty, we propose the use of double cross-validation in the performance evaluation of MoGFS approaches. Double cross-validation has a nested structure of two cross-validation loops. The inner loop is used to determine the best complexity of fuzzy rule-based systems with the highest generalization ability for the training data in each run in the outer loop. That is, the inner loop plays the role of validation data. The determined best complexity is used to choose the final fuzzy rule-based system in each run in the outer loop. We explain the proposed idea by applying it to the performance evaluation of fuzzy rule-based classifiers designed by our multi-objective fuzzy genetics-based machine learning algorithm.","PeriodicalId":120918,"journal":{"name":"2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS)","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2011-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127289573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 8

Workshop on Merging Fields of Computational Intelligence and Sensor Technology (IEEE GEFS 2011) 计算智能与传感器技术融合领域研讨会(IEEE GEFS 2011)

2011 IEEE 5th International Workshop on Genetic and Evolutionary Fuzzy Systems (GEFS) Pub Date : 1900-01-01 DOI: 10.1109/gefs.2011.5949507

Alberto Bugarín-Diz, B. Carse, Fernando Jiménez Barrionuevo

引用次数: 0