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检索条件"任意字段=8th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2007"

共 205 条记录，以下是111-120 订阅

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A Density-Based Clustering Algorithm with Sampling for Travel Behavior Analysis 17th

A Density-Based Clustering Algorithm with Sampling for Trave...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Tang, Wang Pi, Dechang He, Yun Nanjing Univ Aeronaut & Astronaut Coll Comp Sci & Technol 29 Jiangjun Rd Nanjing 211106 Jiangsu Peoples R China

ISBN: (纸本)9783319462578;9783319462561

In order to get the characteristics of travel behavior and reduce the cost of experiment, this paper presents a travel behavior clustering algorithm, sampling-based DBSCAN (SB-DBSCAN), which uses a sampling technique based on density-based spatial clustering of applications with noise ( DBSCAN). By introducing sampling technique, SB-DBSCAN can obtain the approximate distribution of original data set. the improved algorithm solves the problem that DBSCAN needs large capacity memory when analyzing massive data. the effects of sampling rate and neighborhood radius on the efficiency of SB-DBSCAN are analyzed by experiments. After comparing SB-DBSCAN with DBSCAN, the result shows that SB-DBSCAN can maintain the effect of clustering, with a better scalability and less time cost simultaneously. Finally, SB-DBSCAN is used to analyze the travel behavior, which provides decision support for urban planning and construction.

关键词： data mining Cluster analysis Travel behavior Sampling

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Diagnosis Support for Orphan Diseases: A Case Study Using a Classifier Fusion Method 17th

Diagnosis Support for Orphan Diseases: A Case Study Using a ...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Kortum, Xiaowei Grigull, Lorenz Muecke, Urs Lechner, Werner Klawonn, Frank Ostfalia Univ Appl Sci Dept Comp Sci Salzdahlumer Str 46-48 D-38302 Wolfenbuettel Germany Hannover Med Sch Dept Paediat Haematol & Oncol Carl Neuberg Str 1 D-30625 Hannover Germany Improved Med Diagnost IMD GmbH Ostfeldstr 25 D-30559 Hannover Germany Helmholtz Ctr Infect Res Inhoffenstr 7 D-38124 Braunschweig Germany

ISBN: (纸本)9783319462578;9783319462561

Orphan diseases post a particular problem to medical expert and data analysts, because of the lack of data resources and sometimes missing effective treatment. In order to shorten the diagnosing time for rare diseases, we have gathered qualitative and quantitative data through clinical observations, interviews and questionnaires of patients who suffer from rare diseases. From the perspective of data analysis, a pattern recognition system based on an ensemble of classifiers was trained to support the diagnosis of rare diseases. Our study shows that the combination of multiple classifiers has better performance in disease prediction than any individual classifier. Furthermore, a testing method was used to better understand the importance and influences of particular symptoms for certain diseases, and to determine how reliable or sensitive the recognition of diseases through small adjustments in the given answers of a patient.

关键词： Fusion classifier Multiple classifier system Machine learning Pattern recognition

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A MapReduce-Based ELM for Regression in Big data 1

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Wu, B. Yan, T. H. Xu, X. S. He, B. Li, W. H. China Jiliang Univ Sch Mech & Elect Engn Hangzhou 310018 Zhejiang Peoples R China Ocean Univ China Sch Informat Sci Qingdao 266100 Peoples R China Ocean Univ China Engn Coll Qingdao 266100 Peoples R China Univ Wollongong Sch Mech Mat & Mechatron Engn Wollongong NSW Australia

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

Regression is one of the most basic problems in machine learning. In big data era, for regression problem, extreme learning machine (ELM) can get better generalization performance and much fast training speed. However, the enlarging volume of dataset for training makes regression by ELM a challenging task, and it is hard to finish the training in a reasonable time or it will be out of memory. In this paper, through analyzing the theory of ELM, a MapReduce-Based ELM method is proposed. Under the MapReduce framework, ELM submodels are trained in every slave node parallelly. A combination method is designed to combine all the submodels as a complete model. the experiment results demonstrate that the MapReduce-Based ELM can efficient process big dataset on commodity hardware and it has a good performance on speedup under the cloud environment where the dataset is stored as data block in different machines.

关键词： ELM Regression Machine learning Mapreduce Big data

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Faster Decision Tree Induction with Impurity-Based Heuristic Schema 17th

Faster Decision Tree Induction with Impurity-Based Heuristic...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Liu, Junlong Liu, Yunfeng Zhong, Jinhong Shen, Wangyang Univ Sci & Technol China USTC Birmingham Joint Res Inst Intelligent Comput Hefei Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Decision tree is one of the most commonly-used tools in data mining. Most popular induction algorithms construct decision trees in top-down manner. these algorithms generally select splitting feature only with regard to current nodes' data, while ignoring history information. this kind of approaches need to search whole feature space during splitting each node and will be quite time-consuming in high-dimensional cases. To tackle this problem, we propose an impurity-based heuristic schema (IBH) to utilize history information to accelerate existing top-down induction algorithms. In details, when child node's impurity is smaller than parent node's, IBH takes feature performance in parent node as the pseudo upper bound of that in child node, to cut down unpromising computation. the feature selection of IBH biases toward the ones that perform better in parent nodes. Both mathematical analysis and experimental results demonstrate the coherence between IBH and original induction algorithms. Experiments show that IBH can significantly reduce induction time without accuracy degradation in both decision tree and related ensemble methods.

关键词： Decision tree Impurity-based heuristic Feature selection

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Semi-discriminative Multiview Canonical Correlation Analysis for Recognition 17th

Semi-discriminative Multiview Canonical Correlation Analysis...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Yuan, Yun-Hao Li, Yun Ji, Hong-Kun Ren, Chong-Guang Shen, Xiao-Bo Sun, Quan-Sen Yangzhou Univ Sch Informat Engn Yangzhou 225127 Jiangsu Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Jiangsu Peoples R China Shandong Univ Technol Sch Comp Sci & Technol Zibo 255000 Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Different with typical supervised canonical correlation methods where intraclass and interclass information of samples are exploited at the same time for classification tasks, in this paper, we follow the principle of Occam's razor and thus propose a new supervised multiview dimensionality reduction method for image recognition, called semi-discriminative multiview canonical correlations (SemiDMCCs), which takes partial class information into account but generates discriminative low-dimensional projections. Experimental results on benchmark databases show the more effectiveness of the proposed method, in contrast to existing feature reduction methods.

关键词： Pattern recognition Canonical correlation Multiview data learning Discriminative learning

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Uncertain Frequent Itemsets Mining Algorithm on data Streams with Constraints 17th

Uncertain Frequent Itemsets Mining Algorithm on Data Streams...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Yu, Qun Tang, Ke-Ming Tang, Shi-Xi Lv, Xin Hohai Univ Coll Comp & Informat Nanjing Jiangsu Peoples R China Yancheng Teachers Univ Sch Informat Engn Yancheng Peoples R China

ISBN: (纸本)9783319462578;9783319462561

Nowadays, many emerging applications in real-life can produce amount of uncertain data streams, while people are often interested in some aspects. To mine constrained frequent itemsets on uncertain data streams, this paper presents a method. First, determining the order of items in the transactions of data streams according to the properties of constraints;then, inserting items into the tree in order;finally, mining constrained frequent itemsets from the tree. Existing algorithms are compared with the proposed method and the performances are analyzed. Results indicate that the proposed method is effective and efficient, which mines constrained frequent itemsets when users request for the mining results and need no additional memory.

关键词： Uncertain data data streams Frequent itemsets Constraint

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Mining Uplink-Downlink User Association in Wireless Heterogeneous Networks 17th

Mining Uplink-Downlink User Association in Wireless Heteroge...

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Cuzzocrea, Alfredo Grasso, Giorgio M. Jiang, Fan Leung, Carson K. Univ Trieste Trieste TS Italy ICAR CNR Trieste TS Italy Univ Messina Messina ME Italy Univ Manitoba Winnipeg MB Canada

ISBN: (纸本)9783319462578;9783319462561

In the current era of big data, wide varieties of high volumes of valuable data of different veracities can be generated or collected at a high velocity. One of the popular sources of these big data is the wireless networks. Nowadays, the use of smartphones has significantly increased the traffic load in these cellular networks. Consequently, system models that are practical in real-life scenario with the significant for increasing traffic load in cellular networks have drawn attentions of researchers. Studies have been conducted to solve the related interesting research problem of user association in this complex system model. Some of these studies formulated this research problem as a many-to-one matching game, in which users and base stations evaluate each other based on well-defined utilities. In this paper, we examine how the traditional data mining techniques-in particular, the frequent pattern mining techniques-help to solve this research problem. Specifically, we examine the mining of uplink-downlink user association data in wireless heterogeneous networks.

关键词： Association rules Big data data mining Downlink Frequent patterns Knowledge discovery Uplink Wireless heterogeneous networks

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Smart learning Environment for the Development of Smart City Applications 8

Smart Learning Environment for the Development of Smart City...

引用

8th IEEE international conference on intelligent Systems (IS)

作者： Kadar, Manuella 1 Decembrie 1918 Univ Alba Iulia Dept Comp Sci Alba Iulia Romania

ISBN: (纸本)9781509013531

the paper presents the results of the "Smart People for Smart Cities!" project funded by European Structural Funds Human Resources. "1 Decembrie 1918" University of Alba Iulia, Romania in partnership with UTI SA Romania, UNINOVA Portugal and INFOR ELEA Italy have collaborated to bridge and enhance people's skills and competences for the development of smart city applications within the university curricula of Applied Electronics, Computer Science and Environmental engineering specialties. the primary goal was to create an enhanced learning and teaching environment, goal that was achieved through specific objectives: (i.) to set up a research - testing laboratory where real conditions of a smart city is simulated;(ii.) to develop and implement a software instrument for students' evaluation;(iii.) to develop and implement a software solution for processing data provided by various types of sensors used in the smart city environment. the project successfully complemented the academic curricula with a module of practical applications, which simulate existing automation projects on the market, integrates data obtained from sensors and provides statistical models. Smart City Lab (SCL) computing system covers all stages of data processing in automated systems

关键词： Urban computing ubiqitous learning smart city computing system smart environment

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An Effective Genetic Algorithm with Uniform Crossover for Bi-objective Unconstrained Binary Quadratic Programming Problem 1

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Huo, Chao Zeng, Rong-Qiang Wang, Yang Shang, Ming-Sheng Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 610054 Sichuan Peoples R China Southwest Jiaotong Univ Sch Math Chengdu 610031 Sichuan Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Shanxi Peoples R China Chinese Acad Sci Chongqing Inst Green & Intelligent Technol Chongqing 400714 Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

the unconstrained binary quadratic programming problem is one of the most studied NP-hard problem with its various practical applications. In this paper, we propose an effective multi-objective genetic algorithm with uniform crossover for solving bi-objective unconstrained binary quadratic programming problem. In this algorithm, we integrate the uniform crossover within the hypervolume-based multi-objective optimization framework for further improvements. the computational studies on 10 benchmark instances reveal that the proposed algorithm is very effective in comparison with the original multi-objective optimization algorithms.

关键词： Multi-objective optimization Hypervolume contribution Genetic Algorithm Uniform crossover Unconstrained Binary Quadratic Programming problem

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A Compatible Model of Unstructured data for Cross-Media Retrieval in the Field of Tourism 1

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17th international conference on intelligent data engineering and automated learning (ideal)

作者： Hu, Han Li, Xiaoyu Wu, Wenyi Liu, Zhaoyi Univ Elect Sci & Technol China Chengdu Res Inst Chengdu 610054 Sichuan Peoples R China Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Sichuan Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

With the development of multimedia, the amount of unstructured data of multimedia is increasing, especially for the tourism. Meanwhile, it is one of the key issue to analysis large-scale unstructured data, which helps us to find the hidden relevance between redundant and different data. How to retrieve efficiently, and recommend accurately for cross-media retrieval is more and more important. this paper proposes a new data mode for cross-media retrieval - unstructured data compatible model, short for UDC model. the UDC model is constructed by its own metadata. All metadata are organized by a certain hierarchical relationship. Every metadata consists of three layers: the feature layer, the semantic layer and the compatibility layer. Furthermore, this paper presents retrieval and recommendation algorithms based on UDC model. the experiment results demonstrate that the retrieval engine based on UDC mode can be more effective for cross-media retrieval and recommendation.

关键词： Tourism Cross-media Retrieval Recommendation Unstructured data model

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