检索结果-内蒙古大学图书馆

IEEE International Conference on fuzzy systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Varshney, Ayush K. Torra, Vicenc Umea Univ Dept Comp Sci Umea Sweden

ISBN: (数字)9781665467100

ISBN: (纸本)9781665467100

fuzzy rule based classification systems (FRBCSs) are an important area of fuzzy logic and fuzzy sets. FRBCSs provides interpretable models with good classification rate. In the presence of large number of instances with high dimension in training data, the classical Chi's FRBCSs' rulebase becomes huge causing the classical model to be computationally expensive which also leads to the degraded interpretability. In this work, we propose the 'Distributed Chi fuzzy rule based classification systems' (DCHI-FRBCSs). The proposed approach distributes the n-dimensional data into n-nodes where each node forms its own 1-dimensional rules for the dimension. The output from each node is the rule weights which are then aggregated to form the final rule weights, the class with highest final rule weight is the final output of the model. Aggregators play an important role in combining the information from several sources. Andness-directed OWA selects desired level of andness between attributes for OWA aggregator. In this paper, we explore an extension of DCHI-FRBCSs called 'Andess-directed Distributed Chi fuzzy rule based classification system' (alpha-DCHI-FRBCS) which incorporates andness-directed OWA operator as the aggregator function. Results over UCI datasets verify the superiority of our methodology.

关键词： fuzzy rule based classification system Distributed system Andness OWA Aggregators

来源：评论

学校读者我要写书评

暂无评论

A Metahierarchical rule Decision system to Design Robust fuzzy Classifiers based on Data Complexity

引用

IEEE TRANSACTIONS ON fuzzy systemS 2019年第4期27卷 701-715页

作者： Cozar, Javier Fernandez, Alberto Herrera, Francisco Gamez, Jose A. Univ Castilla La Mancha Dept Comp Syst Albacete 02071 Spain Univ Granada Andalusian Res Inst Data Sci & Computat Intellige E-18071 Granada Spain King Abdulaziz Univ Fac Comp & Informat Technol Jeddah 21589 Saudi Arabia

There is a wide variety of studies that propose different classifiers to solve a large amount of problems in distinct classification scenarios. The no free lunch theorem states that if we use a big enough set of varied problems, all classifiers would be equivalent in performance. From another point of view, the performance of the classifiers is dependant of the scope and properties of the datasets. In this sense, new proposals on the topic often focus on a given context, aiming at improving the related state-of-theart approaches. Data complexity metrics have been traditionally used to determine the inner characteristics of datasets. This way, researchers are able to categorize the problems in different scenarios. Then, this taxonomy can be applied to determine inner characteristics of the datasets in order to determine intervals of good and bad behavior for a given classifier. In this paper, we will take advantage of the data complexity metrics in order to design a fuzzy metaclassifier. The final goal is to create decision rules based on the inner characteristics of the data to apply a different version of the fuzzy classifier for a given problem. To do so, we will make use of the FARC-HD classifier, an evolutionary fuzzy system that has led to different extensions in the specialized literature. Experimental results show the goodness of this novel approach as it is able to outperform all versions of FARC-HD on a wide set of problems, and obtain competitive results (in terms of performance and interpretability) versus two selected state-of-the-art rule-based classification system, C4.5 and FURIA.

关键词： Data complexity metrics (DCM) evolutionary fuzzy system fuzzy rule based classification system metaclassifier

来源：评论

学校读者我要写书评

暂无评论

A Parallel fuzzy rule Discovery Algorithm and Future Goods Automated Trading system 36

A Parallel Fuzzy Rule Discovery Algorithm and Future Goods A...

引用

第36届中国控制会议

作者： Zhihao Yang Xiaodong Liu Yashuang Mu Xiaolin Liu The Research Center of Information and Control Dalian University of Technology

ISBN: (纸本)9781538629185

Nowadays,with the rapid development of information technology,many business fields gradually usher in the era of big *** valuable information from the large data is very ***,because of some limitations such as memory restrictions,data complexity or time complexity,the majority of traditional data mining methods are time consuming and low efficiency working on big *** contrast,parallel computing is a common and reliable choice to solve that *** this paper,we propose a fuzzy rule discovery algorithm-fuzzy rule based classification system in the framework of MapReduce(FRBCS-MR).The proposed FRBCS-MR algorithm applies parallel computing technology to extract fuzzy rules and build fuzzy rule-base,which combines the advantage of dealing with uncertainty of fuzzy system and the ability of MapReduce in parallel *** goods trading has been gradually transformed from the original manual trading to stylized trading and the stylized trading is considered to be the forefront and the most scientific mode of *** the experimental studies,the rules extracted by FRBCS-MR algorithm using future goods data are turned into trading strategies which are to be used in the real automated trading platform TradeBIazer(TB) to compute the profit and loss *** result shows that the FRBCS-MR algorithm has good profit ability and possesses a certain guiding significance for investors.

关键词： Big data fuzzy rule based classification system MapReduce Future goods data

来源：评论

学校读者我要写书评

暂无评论

Non-destructive low-cost approach for fuzzy classification of tomato images based on firmness prediciton using regression

引用

JOURNAL OF INTELLIGENT & fuzzy systemS 2017年第5期32卷 3641-3653页

作者： Sehgal, Priti Goel, Nidhi Univ Delhi Keshav Mahavidyalaya Bharat India Univ Delhi Dept Comp Sci Bharat India

Non-destructive techniques such as hyperspectral imaging, backscattering imaging are the advanced techniques used for predicting mechanical properties of horticulture products. They show relatively good performance but at the expense of costly measuring setups. This application-oriented paper investigates the feasibility of employing simple digital color camera imaging for prediction and fuzzy classification of firmness of tomatoes. Images acquired using digital color camera are preprocessed and subject to texture analysis in order to extract the number of features. The proposed approach exploits four texture feature extraction algorithms: three are based on statistical techniques viz. first order statistics (FOS), gray level co-occurrence matrix (GLCM), gray level run length matrix (GLRLM), and one is based on transform-based technique viz. wavelet-transform. Out of all extracted features, redundant features are eliminated using various attribute selection methods. Subsequently, prediction models are built and analyzed using regression analysis. Sample space has been split into two sets;80% training and 20% testing data having tomatoes with almost identical formation. Experimental results illustrates that RBF regression gave the lowest RMSE of 0.174 and highest prediction correlation coefficient of 0.929 for wavelet feature set. Grounded on the prediction model, fuzzy rule based classification (FRBC) is proposed to classify tomatoes into three firmness categories soft, medium, and hard. Accuracy statistics of the proposed FRBCS are compared with the state-of-the-art result and highest classification accuracy of 92.68% is achieved by proposed FRBCS. The results exhibit the possibility of using a digital color imaging system for firmness estimation and further for classification.

关键词： Image texture analysis Tomato firmness fuzzy rule based classification system RBF regression machine vision

来源：评论

学校读者我要写书评

暂无评论

An Extraction Method for the Characterization of the fuzzy rule based classification systems' Behavior using Data Complexity Measures: A case of study with FH-GBML

An Extraction Method for the Characterization of the Fuzzy R...

引用

2010 IEEE World Congress on Computational Intelligence

作者： Luengo, Julian Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain

ISBN: (纸本)9781424469208

When dealing with problems using fuzzy rule based classification systems it is difficult to know in advance whether the model will perform well or badly. In this work we present an automatic extraction method to determine the domains of competence of fuzzy rule based classification systems As a case of study we use the fuzzy Hybrid Genetic based Machine Learning method. We consider twelve metrics of data complexity in order to analyze the behavior patterns of this method, obtaining intervals of such data complexity measures with good or bad performance of it. Combining these intervals we obtain rules that describe both good or bad behaviors of the fuzzy rule based classification system mentioned. These rules allow describe both good or bad behaviors of the fuzzy rule based classification systems mentioned, allowing us to characterize the response quality of the methods from the data set complexity metrics of a given data set. Thus, we can establish the domains of competence of the fuzzy rule based classification systems considered, making it possible to establish when the method will perform well or badly prior to its application.

关键词： FH-GBML automatic extraction method behavior pattern computational complexity data complexity extraction method feature extraction fuzzy hybrid genetic based machine learning method fuzzy rule based classification system fuzzy set theory knowledge based systems pattern classification response quality

来源：评论

学校读者我要写书评

暂无评论

fuzzy Modeling for Vehicle Maneuver Detection in a Scene

Fuzzy Modeling for Vehicle Maneuver Detection in a Scene

引用

2010 IEEE World Congress on Computational Intelligence

作者： Terroso-Saenz, Fernando Valdes-Vela, Mercedes Univ Murcia Dept Informat & Commun Engn DIIC Murcia 30080 Spain

ISBN: (纸本)9781424469208

One of the goals of Advanced Driver Assistance systems (ADASs) is to identify the role of a vehicle in a scene even without Global Positioning system (GPS) information. Some researches solve the problem by implementing different kinematic models for the vehicle along with a mechanism to decide the most suitable model at the current instant. In this work, a fuzzy rule based classification system (FRBCS) takes such decision starting from the measures coming from different sensors in the vehicle. The FRBCS is obtained through Data Driven fuzzy Modeling (DDFM) techniques. In fact, several FRBCSs with promising results are generated. Most discovered models achieve better classification rates than previous researches while being simpler. Therefore, they are more suitable for implementation into an ADAS. Besides, some FRBCSs are far simpler while achieving similar rates. Finally, some tests have been done. They show the feasibility and suitability of this approach behind different situations.

关键词： ADAS FRBCS advanced driver assistance systems data driven fuzzy modeling driver information systems fuzzy rule based classification system fuzzy set theory global positioning system pattern classification vehicle dynamics vehicle maneuver detection

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：