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检索条件"任意字段=10th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2009"

共 105 条记录，以下是21-30 订阅

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Lazy Classification Using an Optimized Instance-Based Learner

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Barbosa, Rui Pedro Belo, Orlando Univ Minho Dept Informat P-4719 Braga Portugal

ISBN: (纸本)9783642043932

Classification is a machine learning technique whose objective is the prediction of the class membership of data instances. there are numerous models Currently available for performing classification. among which decision trees and artificial neural networks. In this article we describe the implementation of a new lazy classification model called similarity classifier. Given an out-of-sample instance, this model predicts its class by finding the training instances that are similar to it, and returning the most frequent class among these instances. the classifier was implemented using Weka's data mining API, and is available for download. Its performance. according to accuracy and speed metrics, compares relatively well with that of well-established classifiers such as nearest neighbor models or support vector machines. For this reason, the similarity classifier can become a useful instrument in a data mining practitioner's tool set.

关键词： data mining supervised learning classification

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Clustering with XCS and Agglomerative Rule Merging

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Shi, Liangdong Shi, Yinghuan Gao, Yang Nanjing Univ State Key Lab Novel Software Technol Nanjing 210008 Jiangsu Peoples R China

ISBN: (纸本)9783642043932

In this paper, we present a more effective approach to clustering with eXtended Classifier System (XCS) which is divided into two phases. the first;phase is the XCS learning process with rule compact, during which we alter the XCS mechanisms and propose a new way to calculate rewards. After learning, the rules are evolved to form the filial population consisting of rules with homogeneous data distribution. the second phase is merging the learnt rules to generate final clusters. We achieve this by modelling the rules as, sub-graphs and merging the sub-graphs based oil some criteria similar to CHAMELEON. Experimental results validate the effectiveness oil a number of datasets, which contain clusters of different shapes, densities and distances.

关键词： clustering learning Classifier System genetic algorithm reinforcement learning rule merging

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Evolving Fuzzy Systems Based on the eTS learning Algorithm for the Valuation of Residential Premises

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Lasota, Tadeusz Telec, Zbigniew Trawinski, Bogdan Trawinski, Krzysztof Wroclaw Univ Environm & Life Sci Dept Spatial Management Ul Norwida 25-27 PL-50375 Wroclaw Poland Wroclaw Univ Technol Inst Informat PL-50370 Wroclaw Poland Edificio Cient Tecnol European Ctr Soft Computing Asturias Spain

ISBN: (纸本)9783642043932

An attempt has been made to employ evolving Takagi-Sugeno algorithm (eTS) to built models assisting property valuation on the basis of actual data drawn from cadastral system, registry of sales transactions, and a cadastral map. Seven methods of feature selection were applied an evaluated. the eTS performance was compared to three algorithms implemented in KEEL, including decision trees for regression. neural network. and Support vector machine. the results confirmed the advantages of the eTS algorithm.

关键词： evolving fuzzy systems eTS learning algorithm property valuation feature selection GIS

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STORM - A Novel Information Fusion and Cluster Interpretation Technique

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Feyereisl, Jan Aickelin, Uwe Univ Nottingham Sch Comp Sci Nottingham NG8 1BB England

ISBN: (纸本)9783642043932

Analysis of data without labels is commonly subject to scrutiny by unsupervised machine learning techniques. Such techniques provide more meaningful representations, useful for better understanding of a problem at hand, than by looking only at the data itself. Although abundant expert knowledge exists in many areas where unlabelled data is examined, such knowledge is rarely incorporated into automatic analysis. Incorporation of expert, knowledge is frequently a matter of combining multiple data sources front disparate hypothetical spaces. In cases where such spaces belong to different, data types, this task becomes even more challenging. In this paper we present a novel immune-inspired method that enables the fusion of such disparate types of data. for a specific set of problems. We show that our method provides a better visual understanding of only hypothetical space with the help of data from another hypothetical space. We believe that, our model has implications for the field of exploratory data analysis and knowledge discovery.

关键词： learning systems

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Implementation and Integration of Algorithms into the KEEL data-Mining Software Tool

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Fernandez, Alberto Luengo, Julian Derrac, Joaquin Alcala-Fdez, Jesus Herrera, Francisco Univ Granada Dept Comp Sci & AI E-18071 Granada Spain

ISBN: (纸本)9783642043932

this work is related to the KEEL1 (Knowledge Extraction based oil Evolutionary learning) tool, a non-commercial software that supports data management, design of experiments and all educational section. the KEEL software tool is devoted to assess evolutionary algorithms for data Mining problems including regression, classification, clustering, pattern mining and so on. these features implies all advantage for the research and educational Field. the aim of this contribution is to present some guidelines for including new algorithms in KEEL helping the researchers to make their methods easily accessible for other authors and to compare the results of many approaches already included within the KEEL software. By providing a source code template, the developer does not need to take into account the basic requirements of the KEEL software tool, and lie or she has only to focus in the designing and encoding of his or hers approach.

关键词： data Mining Evolutionary Algorithms Java Knowledge Extraction Machine learning

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Writer Identification Using a Hybrid Method Combining Gabor Wavelet and Mesh Fractal Dimension

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10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Zhang, Jianjun He, Zhenyu Cheung, Yiu-ming You, Xinge National Anti-counterfeit Engineering Research Center China Department of Electronics and Information Engineering Huazhong University of Science and Technology China Department of Computer Science Hong Kong Baptist University Hong Kong Hong Kong

ISBN: (纸本)9783642043932

Writer identification is to determine the writer from unknown handwritings, which has been becoming an active research topic in pattern recognition field. this paper proposes a new hybrid method combining Gabor function and mesh fractal dimension for off-line, text-in dependent writer identification. Compared to the existing Gabor-based method for off-line, text-independent writer identification, this new method is capable of extracting more useful features to distinguish the handwritings. Experimental results show that this new method can achieve much better identification results than the existing Gabor-based method.

关键词： Character recognition

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Modeling of Network Computing Systems for Decision Tree Induction Tasks

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Walkowiak, Krzysztof Wozniak, Michal Wroclaw Univ Technol Fac Elect Chair Syst & Comp Networks PL-50370 Wroclaw Poland

ISBN: (纸本)9783642043932

Since the amount of information is rapidly growing, there is an overwhelming interest in efficient network computing systems including Grids, public-resource computing systems, P2P systems and Cloud computing. In this paper we take a detailed look at the problem of modeling and optimization of network computing systems for parallel decision tree induction methods. Firstly, we present a comprehensive discussion Oil mentioned induction methods with a special focus on their parallel versions. Next, we propose a generic optimization model of a network computing system that can be used for distributed implementation of parallel decision trees. To illustrate our work we provide results of numerical experiments showing that the distributed approach enables significant improvement of the system throughput.

关键词： Machine learning Network Computing Grids Modeling Optimization Parallel Decision Tree

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Atmospheric Pollution Analysis by Unsupervised learning

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Arroyo, Angel Corchado, Emilio Tricio, Veronica Univ Burgos Dept Civil Engn Burgos Spain Univ Burgos Dept Phys Burgos Spain

ISBN: (纸本)9783642043932

this paper presents a multidisciplinary study on the application of statistical and neural models for analysing data oil immissions of atmospheric Pollution ill urban areas. data was collected from the network of pollution measurement stations in the Spanish Autonomous Region of Castile-Leon. Four Pollution parameters and a Pollution measurement station in the city of Burgos were used to carry Out the Study in 2007, during a period Of just over six months. Pollution data are compared, their values are interrelated and relationships are established not only with the Pollution variables, but also with different weeks of the year. the aim of this Study is to classify the levels of atmospheric Pollution in relation to the days of the week, trying to differentiate between working days and non-working days.

关键词： artificial neural networks meteorology pollution

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Requirements engineering in the Development of Multi-Agent Systems: A Systematic Review

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Blanes, David Insfran, Emilio Abrahao, Silvia Univ Politecn Valencia ISSI Res Grp Dept Informat Syst & Computat Camino Vera S-N Valencia 46022 Spain

ISBN: (纸本)9783642043932

the goal of this paper is to investigate which requirements engineering techniques have been applied in the development of Multi-Agent Systems (MAS) and how they were applied. We performed a systematic review of 58 of a total of 835 papers found in scientific digital libraries. the results show that most of the proposals for dealing with requirements (79%) use already defined methods or techniques from other software development paradigms and that 69% of these techniques are based on the goal-oriented paradigm. A total of 95% of the reviewed papers focus on techniques for analyzing requirements, and only 45% of them explicitly consider some type of elicitation technique. Finally, only 5% of the papers give some empirical evidence about the effectiveness of their approaches by conducting empirical studies. the results of our study are particularly important in the determination of current research activities in Requirements engineering for MAS and in the identification of research gaps for further investigation.

关键词： requirements engineering multi-agent systems systematic review

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Interaction Detection in Aerodynamic Design data

引用

10th international conference on intelligent data engineering and automated learning (ideal 2009)

作者： Graening, Lars Olhofer, Markus Sendhoff, Bernhard Honda Res Inst Europe GmbH D-63073 Offenbach Germany

ISBN: (纸本)9783642043932

In large and complex aerodynamic systems the overall performance of a design is mainly defined by interactions between design areas rather than by single design regions. therefore it is necessary to identify these interactions in order to be able to understand and imp rove the designs. However. detecting and modeling those interactive effects between distant design areas is a very challenging task which usually requires a detailed understanding of the flow patterns and dedicated expert knowledge. In this paper we apply the information theoretic concept of interaction information to aerodynamic design data in order to detect and quantify interaction effects between distant design regions. Information graphs are suggested in order to provide the results to the aerodynamic engineer in a graphical form. In order to show the feasibility of this approach, the information theoretic quantities are applied to the data of a 2D wing assembly as well as to the 3D turbine blade design data.

关键词： Knowledge Extraction Interaction Detection Information theory Aerodynamic Design data Turbine Blade

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