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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 204 条记录，以下是91-100 订阅

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Fuzzy Clustering Approach to data Selection for Computer Usage in Headache Disorders 20th

Fuzzy Clustering Approach to Data Selection for Computer Usa...

引用

20th international conference on intelligent data engineering and automated learning (ideal)

作者： Simic, Svetlana Radmilo, Ljiljana Simic, Dragan Simic, Svetislav D. Tallon-Ballesteros, Antonio J. Univ Novi Sad Fac Med Hajduk Veljkova 1-9 Novi Sad 21000 Serbia Gen Hosp Dr Radivoj Simonovic Sombor 25000 Serbia Univ Novi Sad Fac Tech Sci Trg Dositeja Obradovica 6 Novi Sad 21000 Serbia Univ Huelva Dept Elect Comp Syst Engn & Automat Calle Dr Cantero Cuadrado 6 Huelva 21004 Spain

ISBN: (纸本)9783030336172;9783030336165

this paper is focused on a new approach based on fuzzy clustering system for diagnosing headache disorders. the proposed fuzzy clustering system is based on two steps Gustafson-Kessel clustering system. Experimental data set consist of the frequency of the computer use and habits while using the computer and assessment of the adverse health effects due to computer use. the attribute selection for major features is done based on the experimental data set. the proposed fuzzy clustering system is tested on data set collected from patients in Clinical Centre of Vojvodina, in Novi Sad, Serbia.

关键词： Attribute selection Fuzzy clustering Gustafson-Kessel algorithm

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A Deep learning Approach for Location Independent throughput Prediction 8

A Deep Learning Approach for Location Independent Throughput...

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8th IEEE international conference on Connected Vehicles and Expo (ICCVE)

作者： Schmid, Josef Schneider, Mathias Hoess, Alfred Schuller, Bjoern Ostbayr TH Amberg Weiden D-92224 Amberg Germany Univ Augsburg ZDB Chair Embedded Intelligence Hlth Care & Wellb D-86159 Augsburg Germany

ISBN: (纸本)9781728101422

Mobile communication has become a part of everyday life and is considered to support reliability and safety in traffic use cases such as conditionally automated driving. Nevertheless, prediction of Quality of Service parameters, particularly throughput, is still a challenging task while on the move. Whereas most approaches in this research field rely on historical data measurements, mapped to the corresponding coordinates in the area of interest, this paper proposes a throughput prediction method that focuses on a location independent approach. In order to compensate the missing positioning information, mainly used for spatial clustering, our model uses low-level mobile network parameters, improved by additional feature engineering to retrieve abstracted location information, e.g., surrounding building size and street type. thus, the major advantage of our method is the applicability to new regions without the prerequisite of conducting an extensive measurement campaign in advance. therefore, we embed analysis results for underlying temporal relations in the design of different deep neuronal network types. Finally, model performances are evaluated and compared to traditional models, such as the support vector or random forest regression, which were harnessed in previous investigations.

关键词： throughput prediction deep learning cellular network

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Modeling data Driven Interactions on Property Graph 1

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20th international conference on intelligent data engineering and automated learning (ideal)

作者： Pongpech, Worapol Alex NIDA Business Analyt & Data Sci Grad Program Fac Appl Stat Bangkok 10240 Thailand

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

data Driven Transformation is a process where an organization transforms its infrastructure, strategies, operational methods, technologies, or organizational culture to facilitate and encourage data driven decision-making behaviors. We defined a data driven network as group/groups of people in an organization network working on common projects where making data driven decisions is necessary in the project. One important problem in data driven transformation is how to understand data driven behaviors in the network. Ability to model and compute on a DDIG is crucial for understanding data driven network behaviors. To this end, we modeled a data driven network as a property graph, in which nodes represent users, projects, or data sources and edges represent interactions between them. We termed this data Driven Interactions Graph and modeled on the Neo4j graph platform. A graph model framework to represent data driven interactions such that a number of network properties can be computed from the proposed graph is introduced. Finally, we discussed data utilization and data collaboration behaviors of the social working network.

关键词： data Driven Transformation Collaboration Utilization Property graph Social network analysis Clustering Communities Neo4j

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the influence of sampling on imbalanced data classification 8

The influence of sampling on imbalanced data classification

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8th Brazilian conference on intelligent Systems, BRACIS 2019

作者： Barella, Victor H. Garcia, Luís P.F. De Carvalho, André C.P.L.F. ICMC/University of São Paulo São Carlos Brazil CIC/University of Brasilia Brasília Brazil

ISBN: (纸本)9781728142531

Classification tasks using imbalanced data are not challenging on their own. When the classes are linearly separable, a regular classification algorithm usually induces predictive models able to distinguish the classes properly. Imbalanced data poses difficulty for the minority class when the training sets have classes overlapping or a complex border decision. Assessing these characteristics is fundamental to understand the classification task difficulty and to choose adequate pre-processing techniques for imbalanced data. Measures able to identify the complexity of a classification task for a given dataset have been proposed. these measures use different criteria to identify how difficult it is to induce a classifier from a dataset. In this paper, we investigate the use of data complexity measures to estimate the best sample size for data imbalance pre-processing techniques. For such, this paper assesses the predictive performance and the data complexity of real datasets after applying pre-processing techniques using different sample sizes. According to experiments, the data complexity measures are a tool to help in choosing a proper sample size to improve the predictive performance of the classifiers. We also observe that only the difficulty of predicting the minority class is not enough when dealing with sampling. As an alternative to deal with this deficiency, we suggest a combination of the data complexity of both classes. © 2019 IEEE.

关键词： Sampling

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data Set Partitioning in Evolutionary Instance Selection 19th

Data Set Partitioning in Evolutionary Instance Selection

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Kordos, Miroslaw Czepielik, Lukasz Blachnik, Marcin Univ Bielsko Biala Dept Comp Sci & Automat Willowa 2 Bielsko Biala Poland Silesian Tech Univ Dept Appl Informat Krasinskiego 8 Katowice Poland

ISBN: (纸本)9783030034931;9783030034924

Evolutionary instance selection outperforms in most cases non-evolutionary methods, also for function approximation tasks considered in this work. However, as the number of instances encoded into the chromosome grows, finding the optimal subset becomes more difficult, especially that running the optimization too long leads to over-fitting. A solution to that problem, which we evaluate in this work is to reduce the search space by clustering the dataset, run the instance selection algorithm for each cluster and combine the results. We also address the issue of properly processing the instances close to the cluster boundaries, as this is where the drop of accuracy can appear. the method is experimentally verified on several regression datasets with thousands of instances.

关键词： Chromosomes

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Applying Cost-Sensitive Classifiers with Reinforcement learning to IDS 19th

Applying Cost-Sensitive Classifiers with Reinforcement Learn...

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Blanco, Roberto Cilla, Juan J. Briongos, Samira Malagon, Pedro Moya, Jose M. Univ Politecn Madrid ETSI Telecomunicac Integrated Syst Lab Ave Complutense 30 Madrid 28040 Spain Univ Politecn Madrid Ctr Computat Simulat Campus Montegancedo Madrid 28660 Spain

ISBN: (纸本)9783030034931;9783030034924

When using an intrusion detection system as protection against certain kind of attacks, the impact of classifying normal samples as attacks (False Positives) or attacks as normal traffic (False Negatives) is completely different. In order to prioritize the absence of one kind of error, we use reinforcement learning strategies which allow us to build a cost-sensitive meta-classifier. this classifier has been build using a DQN architecture over a MLP. While the DQN introduces extra effort during the training steps, it does not cause any penalty on the detection system. We show the feasibility of our approach for two different and commonly used datasets, achieving reductions up to 100% in the desired error by changing the rewarding strategies.

关键词： Intrusion detection Cost-sensitive classification Reinforcement learning

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Handwritten Character Recognition Using Active Semi-supervised learning 1

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19th international conference on intelligent data engineering and automated learning (ideal)

作者： Inkeaw, Papangkorn Bootkrajang, Jakramate Goncalves, Teresa Chaijaruwanich, Jeerayut Chiang Mai Univ Dept Comp Sci Fac Sci Chiang Mai 50200 Thailand Univ Evora Dept Informat P-7000671 Evora Portugal

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

Constructing a handwritten character recognition model is considered challenging partly due to the high variety of handwriting styles and the limited amount of training data. In practice, only a handful of labeled examples from limited number of writers are provided during the training of the model. Still, a large collection of already available unlabeled handwritten character data from several sources are often left unused. To alleviate the problem of small training sample size, we propose a graph-based active semi-supervised learning approach for handwritten character recognizer construction. the method iteratively builds a neighborhood graph of all examples including the unlabeled ones, assigns pseudo labels to the unlabeled data and retrains the model. Additionally, the label of the least confident pseudo label according to a newly proposed uncertainty measure is to be requested from the oracle. Experiments on NIST handwritten digits dataset demonstrated that the proposed learning method better utilizes the unlabeled data compared to existing approaches as measured by recognition accuracy. In addition, our active learning strategy is also more effective compared to baseline strategies.

关键词： Handwritten character recognition Semi-supervised learning Active learning

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Improving software automation testing using Jenkins, and machine learning under big data 8th

Improving software automation testing using Jenkins, and mac...

引用

8th international conference on Big data Technologies and Applications, BDTA 2017

作者： Stouky, Ali Jaoujane, Btissam Daoudi, Rachid Chaoui, Habiba Laboratoire Génie Des Système L’équipe ADSI ENSA de Kénitra Kenitra Morocco

ISBN: (纸本)9783319987514

Software testing is an essential phase of software development life cycle that ensures quality of the software by fixing bugs which can be done with automated testing to reduce human intervention and to save time and effort consumed in the manual testing. the entire process of testing can be automated easily with the help of automated testing tools. this paper provides a feasibility study for the most commonly used testing tools, we conducted a comparative study of five open source tools to determine their usability and effectiveness. Another point discussed in our paper is the use of machine learning under big data in order to make the system intelligent so that tests lend themselves to automation. We will show how can the combination of all these mentioned technologies can help users to decide which strategy to go for to save both cost and time during testing phases. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering 2018.

关键词： Software testing

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Organized behavior classification of tweet sets using supervised learning methods 18

Organized behavior classification of tweet sets using superv...

引用

8th international conference on Web Intelligence, Mining and Semantics, WIMS 2018

作者： Beğenilmiş, Erdem Uskudarli, Suzan Bogazici University Istanbul Turkey

ISBN: (纸本)9781450354899

there is an increasing incidence in negative propaganda and fake news, which has recently gained lots of attention during the 2016 elections in United States, France, and United Kingdom. Bots and hired users collaborate to make messages seen and persist so they may spread and gain support. Assuming that most Twitter users post without predetermined, malicious intent, there is a need for automated detection of organized behavior to protect users from manipulation. this work proposes an automated approach to classify tweets with organized behavior. Supervised learning methods are used to classify the tweets by using a training data set with 850 records based on the analysis of over 200 million tweets. Our model gave promising results for detection of organized behavior and this motivated us to proceed with the generation of two more classifiers such as ["political", "non-political"] and ["pro-Trump", "pro-Hillary","neither"]. In each cases, the random forest algorithm consistently results in high scores with an average accuracy and f-measure above 0.95. © 2018 Association for Computing Machinery.

关键词： Supervised learning

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From Parzen Window Estimation to Feature Extraction: A New Perspective 1

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

作者： Liu, Zhong-bao Zhang, Jing Song, Wen-ai North Univ China Sch Software Taiyuan 030051 Peoples R China

ISBN: (数字)9783319462578

ISBN: (纸本)9783319462578;9783319462561

Researches on current feature extraction methods are mainly based on two ways. One originates from geometric properties of high-dimensional datasets and attempt to extract fewer features from the original data space according to a certain criterion. the other originates from dimension reduction deviation and tries to make the deviation between data before and after dimension reduction be as small as possible. However, there exists almost no any study about them from the perspective of the scatter change of a dataset. Based on Parzen window density estimator, the relevant feature extraction methods are thoroughly revisited from a new perspective and the relations between Parzen window and LPP and LDA are built in this paper.

关键词： Feature extraction Parzen window data distribution characteristics New perspective

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