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检索条件"任意字段=15th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2014"

共 118 条记录，以下是11-20 订阅

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Towards data Mart Building from Social Network for Opinion Analysis

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Moalla, Imen Nabli, Ahlem Sfax Univ Fac Sci Rd Sokra Km 3 BP 1171 Sfax 3000 Tunisia MIRACL Lab Sfax Tunisia

ISBN: (纸本)9783319108407;9783319108391

In the recent years, social networks have played a strategic role in the lives of many companies. therefore several decision makers have worked on these networks for making better decisions. Furthermore, the increased interaction between social networks and web users has lead many companies to use a data warehouse to collect information about their fans. this paper deals with a multidimensional schema construction from unstructured data extracted from social network. this construction is carried out from Facebook page in order to analyze customers' opinions. A real case study has been developed to illustrate the proposed method and confirming that he social network analysis can predict chance of the success of products.

关键词： data warehouse data Mart Social Networks analysis Opinion Analysis Schema Design

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Zero-Latency data Warehouse System Based on Parallel Processing and Cache Module

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Gorawski, Marcin Lis, Damian Gorawska, Anna Silesian Tech Univ Inst Comp Sci PL-44100 Gliwice Poland

ISBN: (纸本)9783319108407;9783319108391

Zero-Latency data Warehouse (ZLDW) cannot be developed and formed on the basis of a standard ETL process, where time frames are limiting access to current data and blocking the ability to take users needs into account. therefore, after profound analysis of this issue and ones related to workload balancing, an innovative system based on a Workload Balancing Unit (WBU) was created. In this paper we present innovative workload balancing algorithm - CTBE (Choose Transaction By Election), which allows to analyze all incoming transactions and create a schema of dependencies between them. Also, cache in the created WBU ensures ability to store information on incoming transactions and exchange messages with systems transmitting updates and users' queries. By this work we intend to present an innovative system designed to support Zero-Latency data Warehouse.

关键词： Cache CTBE algorithm ETL Workload Balancing WBU WINE-HYBRIS algorithm Zero-Latency data Warehouse

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Automatic Validation of Flowmeter data in Transport Water Networks: Application to the ATLLc Water Network

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Garcia, Diego Quevedo, Joseba Puig, Vicenc Saludes, Jordi Espin, Santiago Roquet, Jaume Valero, Fernando UPC Adv Control Syst SAC Campus TerrassaRambla St Nebridi 10 Barcelona 08222 Spain ATLL Concessionaria Generalitat Catalunya S Sant E-08970 Barcelona Spain

ISBN: (纸本)9783319108407;9783319108391

In this paper, a methodology for data validation and reconstruction of flow meter sensor data in water networks is presented. the raw data validation is inspired on the Spanish norm (AENOR-UNE norm 500540). the methodology consists in assigning a quality level to data. these quality levels are assigned according to the number of tests that data have passed. the methodology takes into account not only spatial models but also temporal models relating the different sensors. the methodology is applied to real-data acquired from the ATLLc Water Network. the results demonstrate the performance of the proposed methodology in detecting errors in measurements and in reconstructing them.

关键词： Flowmeters

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A Drowsy Driver Detection System Based on a New Method of Head Posture Estimation

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Teyeb, Ines Jemai, Olfa Zaied, Mourad Ben Amar, Chokri Univ Sfax Natl Engn Sch Sfax REs Grp Intelligent Machines REGIM Lab Sfax 3038 Tunisia

ISBN: (纸本)9783319108407;9783319108391

A drowsy driver detection system based on a new method for head posture estimation is proposed. In the first part, we introduced six possible models of head positions that can be detected by our algorithm which is explained in the second part. Indeed, there are three key stages characterizing our method: First of all, we proceed with driver's face detection by Viola and Jones algorithm. then, we extract the image reference and the non image reference coordinates from the face bounding's box. Finally, based on measuring both the head inclination's angle and distances between the extracted coordinates, we classify the head state (normal or inclined). Test results demonstrate that the proposed system can efficiently measure the aforementioned parameters and detect the head state as a sign of driver's drowsiness.. . .

关键词： Drowsiness detection head posture analysis face detection

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Sample Size Issues in the Choice between the Best Classifier and Fusion by Trainable Combiners

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Raudys, Sarunas Fumera, Giorgio Raudys, Aistis Pillai, Ignazio Vilnius State Univ Fac Math & Informat Naugarduko St 24 LT-03225 Vilnius Lithuania Univ Cagliari Dept Elect & Elect Eng I-09123 Cagliari Italy

ISBN: (纸本)9783319108407;9783319108391

We consider an open issue in the design of pattern classifiers, i.e., choosing between the best classifier among a given ensemble, and combining all the available ones using a trainable fusion rule. While the latter choice can in principle outperform the former, their actual effectiveness is affected by small sample size problems. this raises the need of investigating under which conditions one choice is better than the other one. We provide a first contribution, by deriving an analytical expressions of the expected error probability of best classifier selection, and by comparing it with the one of a well known linear fusion rule, implemented with the Fisher linear discriminant.

关键词： data dimensionality sample size complexity collective decision expert fusion accuracy classification

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Linear Regression Fisher Discrimination Dictionary learning for Hyperspectral Image Classification

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Chen, Liang Yang, Ming Deng, Cheng Yin, Hujun Nanjing Normal Univ Sch Comp Sci & Technol Nanjing Jiangsu Peoples R China Univ Manchester Sch Elect & Elect Engn Manchester M13 9PL Lancs England

ISBN: (纸本)9783319108407;9783319108391

In this paper, we propose a novel dictionary learning method for hyperspectral image classification. the proposed method, linear regression Fisher discrimination dictionary learning (LRFDDL), obtains a more discriminative dictionary and a classifier by incorporating linear regression term and the Fisher discrimination into the objective function during training. the linear regression term makes predicted and actual labels as close as possible;while the Fisher discrimination is imposed on the sparse codes so that they have small with-class scatters but large between-class scatters. Experiments show that LRFDDL significantly improves the performances of hyperspectral image classification.

关键词： Dictionary learning Linear Regression Fisher Discrimination Hyperspectral Image Classification

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LBP and Machine learning for Diabetic Retinopathy Detection

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： de la Calleja, Jorge Tecuapetla, Lourdes Auxilio Medina, Ma Barcenas, Everardo Urbina Najera, Argelia B. Univ Politecn Puebla Mexico City 72640 DF Mexico

ISBN: (纸本)9783319108407;9783319108391

Diabetic retinopathy is a chronic progressive eye disease associated to a group of eye problems as a complication of diabetes. this disease may cause severe vision loss or even blindness. Specialists analyze fundus images in order to diagnostic it and to give specific treatments. Fundus images are photographs taken of the retina using a retinal camera, this is a noninvasive medical procedure that provides a way to analyze the retina in patients with diabetes. the correct classification of these images depends on the ability and experience of specialists, and also the quality of the images. In this paper we present a method for diabetic retinopathy detection. this method is divided into two stages: in the first one, we have used local binary patterns (LBP) to extract local features, while in the second stage, we have applied artificial neural networks, random forest and support vector machines for the detection task. Preliminary results show that random forest was the best classifier with 97.46% of accuracy, using a data set of 71 images.

关键词： machine learning local binary patterns medical image analysis

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Managing Borderline and Noisy Examples in Imbalanced Classification by Combining SMOTE with Ensemble Filtering

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Saez, Jose A. Luengo, Julian Stefanowski, Jerzy Herrera, Francisco Univ Granada CITIC UGR Dept Comp Sci & Artificial Intelligence E-18071 Granada Spain Univ Burgos Dept Civil Engn LSI E-09006 Burgos Spain Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

ISBN: (纸本)9783319108407;9783319108391

Imbalance data constitutes a great difficulty for most algorithms learning classifiers. However, as recent works claim, class imbalance is not a problem in itself and performance degradation is also associated with other factors related to the distribution of the data as the presence of noisy and borderline examples in the areas surrounding class boundaries. this contribution proposes to extend SMOTE with a noise filter called Iterative-Partitioning Filter (IPF), which can overcome these problems. the properties of this proposal are discussed in a controlled experimental study against SMOTE and its most well-known generalizations. the results show that the new proposal performs better than exiting SMOTE generalizations for all these different scenarios.

关键词： Classification imbalanced data SMOTE class noise noise filters

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MLeNN: A First Approach to Heuristic Multilabel Undersampling

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Charte, Francisco Rivera, Antonio J. del Jesus, Maria J. Herrera, Francisco Univ Granada Dept Comp Sci & Artificial Intelligence Granada Spain Univ Jaen Dep Comp Sci Jaen Spain

ISBN: (纸本)9783319108407;9783319108391

learning from imbalanced multilabel data is a challenging task that has attracted considerable attention lately. Some resampling algorithms used in traditional classification, such as random undersampling and random oversampling, have been already adapted in order to work with multilabel datasets. In this paper MLeNN (MultiLabel edited Nearest Neighbor), a heuristic multilabel undersampling algorithm based on the well-known Wilson's Edited Nearest Neighbor Rule, is proposed. the samples to be removed are heuristically selected, instead of randomly picked. the ability of MLeNN to improve classification results is experimentally tested, and its performance against multilabel random undersampling is analyzed. As will be shown, MLeNN is a competitive multilabel undersampling alternative, able to enhance significantly classification results.

关键词： Multilabel Classification Imbalanced learning Resampling ENN

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Fast Frequent Pattern Detection Using Prime Numbers

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15th international conference on intelligent data engineering and automated learning (ideal)

作者： Xylogiannopoulos, Konstantinos F. Addam, Omar Karampelas, Panagiotis Alhajj, Reda Univ Calgary Dept Comp Sci Calgary AB T2N 1N4 Canada Hellen Amer Univ Dept Informat Technol Manchester NH USA Global Univ Dept Comp Sci Beirut Lebanon Hellen Air Force Acad Dept Informat Computers Attica Greece

ISBN: (纸本)9783319108407;9783319108391

Finding all frequent itemsets (patterns) in a given database is a challenging process that in general consumes time and space. Time is measured in terms of the number of database scans required to produce all frequent itemsets. Space is consumed by the number of potential frequent itemsets which will end up classified as not frequent. To overcome both limitations, namely space and time, we propose a novel approach for generating all possible frequent itemsets by introducing a new representation of items into groups of four items and within each group, items are assigned one of four prime numbers, namely 2, 3, 5, and 7. the reported results demonstrate the applicability and effectiveness of the proposed approach. Our approach satisfies scalability in terms of number of transactions and number of items.

关键词： frequent pattern mining association rules data mining prime representation

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