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检索条件"任意字段=11th International Conference on Intelligent Data Engineering and Automated Learning"

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Reusable Big data System for Industrial data Mining - A Case Study on Anomaly Detection in Chemical Plants 19th

Reusable Big Data System for Industrial Data Mining - A Case...

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

作者： Borrison, Reuben Kloepper, Benjamin Chioua, Moncef Dix, Marcel Sprick, Barbara ABB Corp Res Ctr Germany Ladenburg Germany SRH Hsch Heidelberg Heidelberg Germany

ISBN: (纸本)9783030034931;9783030034924

Industrial data mining projects in general and big data mining projects in particular suffer from long project execution. the resulting high costs render many interesting use cases otherwise economically unattractive. this contribution shows on the example of anomaly detection for process plants, how the major obstacles - namely the inefficient development tools for Big data Frameworks like Apache Hadoop and Spark and the lack of reuse of software artifacts across different projects can be overcome. this is achieved by selecting an application case that shares considerable commonalities across different projects and providing a supported project workflow implemented in a scalable and extensible big data architecture.

关键词： Big data data mining Software architecture

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CCTV Image Sequence Generation and Modeling Method for Video Anomaly Detection Using Generative Adversarial Network 19th

CCTV Image Sequence Generation and Modeling Method for Video...

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

作者： Shin, Wonsup Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030034931;9783030034924

Video anomaly detection is one of the most attractive problem in various fields likes computer vision. In this paper, we propose a VAD classifier modeling method that learns in a supervised learning manner. the basic idea is to solve the problem of labeled data shortage through transfer learning. the key idea is to create an underlying model of transfer learning through the GAN of discriminator. We solved this problem by proposing a GAN model consisting of a generator that generates video sequences and a discriminator that follows LRCN structure. As a result of the experiment, the VAD classifier learned through GAN-based transfer learning obtained higher accuracy and recall than the pure LRCN classifier and other machine learning methods. Additionally, we demonstrated that the generator be able to stably generate the image similar to the actual data as the learning progressed. To the best of our knowledge, this paper is the first case to solve the VAD problem using the GAN model and the supervised learning manner.

关键词： Autonomous CCTV surveillance Video anomaly detection Transfer learning Supervised learning Generative adversarial network

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Towards Complex Features: Competitive Receptive Fields in Unsupervised Deep Networks 19th

Towards Complex Features: Competitive Receptive Fields in Un...

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

作者： Hankins, Richard Peng, Yao Yin, Hujun Univ Manchester Sch Elect & Elect Engn Manchester Lancs England

ISBN: (纸本)9783030034931;9783030034924

In this paper we propose a simple unsupervised approach to learning higher order features. this model is based on the recent success of lightweight approaches such as SOMNet and PCANet to the challenging task of image classification. Contrary to the more complex deep learning models such as convolutional neural networks (CNNs), these methods use naive algorithms to model the input distribution. Our endeavour focuses on the self-organizing map (SOM) based method and extends it by incorporating a competitive connection layer between filter learning stages. this simple addition encourages the second filter learning stage to learn complex combinations of first layer filters and simultaneously decreases channel depth. this approach to learning complex representations offers a competitive alternative to common deep learning models whilst maintaining an efficient framework. We test our proposed approach on the popular MNIST and challenging CIFAR-10 datasets.

关键词： Self-organising maps Unsupervised learning Deep learning Representation learning Receptive fields Pooling

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Recursive Implementation of Gaussian Process Regression for Spatial-Temporal data Modeling

Recursive Implementation of Gaussian Process Regression for ...

引用

international conference on Wireless Communications and Signal Processing (WCSP)

作者： Ye Kuang Tianshi Chen Feng Yin Renxin Zhong School of Science and Engineering Shenzhen Research Institute of Big Data The Chinese University of Hong Kong Shenzhen Shenzhen China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China

In this paper, we consider the spatial-temporal data modeling problem with large number of time instants and moderate number of locations. the problem is formulated as a function estimation problem and then handled by the Gaussian process regression method. To lower the computational complexity, we first sample the continuous-time kernel to get a discrete-time kernel, and then we derive its discrete-time state-space model realization, which is free of numerical problems. then we convert the Gaussian process regression problem to a Kalman filtering and smoothing problem including both the hyper-parameter estimation and prediction. We consider three hyper-parameter estimation methods: the marginal likelihood maximization method, the generalized cross validation method, and the Stein's unbiased risk estimation method. the proposed implementation is tested over a simulated data set and the Colorado weather data set.

关键词： Gaussian processes Estimation Kernel Kalman filters State-space methods Computational complexity Contracts

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Boosting Neural Commit Message Generation with Code Semantic Analysis

Boosting Neural Commit Message Generation with Code Semantic...

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IEEE international conference on automated Software engineering (ASE)

作者： Shuyao Jiang School of Computer Science Fudan University Shanghai China Shanghai Institute of Intelligent Electronics & Systems Shanghai China

It has been long suggested that commit messages can greatly facilitate code comprehension. However, developers may not write good commit messages in practice. Neural machine translation (NMT) has been suggested to automatically generate commit messages. Despite the efforts in improving NMT algorithms, the quality of the generated commit messages is not yet satisfactory. this paper, instead of improving NMT algorithms, suggests that proper preprocessing of code changes into concise inputs is quite critical to train NMT. We approach it with semantic analysis of code changes. We collect a real-world dataset with 50k+ commits of popular Java projects, and verify our idea with comprehensive experiments. the results show that preprocessing inputs with code semantic analysis can improve NMT significantly. this work sheds light to how to apply existing DNNs designed by the machine learning community, e.g., NMT models, to complete software engineering tasks.

关键词： Software engineering Semantics Software data preprocessing Task analysis conferences Manuals

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Imbalanced data Classification Based on Feature Selection Techniques 19th

Imbalanced Data Classification Based on Feature Selection Te...

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

作者： Ksieniewicz, Pawel Wozniak, Michal Wroclaw Univ Sci & Technol Dept Syst & Comp Networks Wroclaw Poland

ISBN: (纸本)9783030034962;9783030034955

the difficulty of the many classification tasks lies in the analyzed data nature, as disproportionate number of examples from different class in a learning set. Ignoring this characteristics causes that canonical classifiers display strongly biased performance on imbalanced datasets. In this work a novel classifier ensemble forming technique for imbalanced datasets is presented. On the one hand it takes into consideration selected features used for training individual classifiers, on the other hand it ensures an appropriate diversity of a classifier ensemble. the proposed method was tested on the basis of the computer experiments carried out on the several benchmark datasets. their results seem to confirm the usefulness of the proposed concept.

关键词： Machine learning Classification Imbalanced data Feature selection Random search

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Single-Class Bankruptcy Prediction Based on the data from Annual Reports 19th

Single-Class Bankruptcy Prediction Based on the Data from An...

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

作者： Drotar, Peter Gnip, Peter Zoricak, Martin Gazda, Vladimir Tech Univ Kosice Fac Elect Engn & Informat Letna 9 Kosice Slovakia Tech Univ Kosice Fac Econ Nemcovej 32 Kosice Slovakia

ISBN: (纸本)9783030034931;9783030034924

the companies involved in all areas of the business and industry can due to the unfavourable financial situation or inappropriate investments face financial problems resulting in bankruptcy of the company. the ability to foresee imminent bankruptcy helps managers and stock holders to take the corrective actions. In this paper, we analyze annual reports of thousands of limited liability companies and propose the bankruptcy prediction model. the available dataset is strongly imbalanced that corresponds to the real-world situation where bankrupt companies constitute only a small fraction of all companies. the proposed model is based on single-class least-squares anomaly detection classifier achieving as high as 91% prediction accuracy.

关键词： Anomaly detection Novelty detection Imbalanced learning Bankruptcy Least-squares anomaly detection

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Cross-cohort evaluation of machine learning approaches to fall detection from accelerometer data 11

Cross-cohort evaluation of machine learning approaches to fa...

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11th international conference on Health Informatics, HEALthINF 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Lisowska, Aneta O’Neil, Alison Poole, Ian Toshiba Medical Visualization Systems Europe Ltd. 2 Anderson Place Edinburgh United Kingdom School of Engineering and Physical Sciences Heriot-Watt University Edinburgh United Kingdom

ISBN: (纸本)9789897582813

Falls in seniors can lead to serious physical and psychological consequences. A fall detector can allow a fallen person to receive medical intervention promptly after the incident. the accelerometer data from smartphones or wearable devices can be used to detect falls without serious privacy intrusion. Common machine learning approaches to fall detection include supervised and novelty based methods. Previous studies have found that supervised methods have superior performance when tested on participants from the population cohort resembling the one they were trained on. In this study, we investigate if the performance remains superior when they are tested on a distinctly different population cohort. We train the supervised algorithms on data gathered using a wearable Silmee device (Cohort 1) and test on smartphone data from a publicly available data set (Cohort 2). We show that the performance of the supervised methods decreases when they are tested on distinctly different data, but that the decrease is not substantial. Novelty based fall detectors have better performance, suggesting that novelty based detectors might be better suited for real life applications. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Smartphones

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learning Feedback Linearization Based Stable Robust Adaptive NARMA Controller Design for Rotary Inverted Pendulum

Learning Feedback Linearization Based Stable Robust Adaptive...

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international conference on Electrical and Electronics engineering, ELECO

作者： Mehmet Uğur Soydemir Savaş Şahin Parvin Bulucu Aykut Kocaoğlu Cüneyt Güzeliş Electrical and Electronics Engineering İzmir Katip Çelebi University İzmir Turkey The Institute of Natural and Applied Sciences Dokuz Eylül University İzmir Turkey Electrical Program Dokuz Eylül University İzmir Turkey Electrical and Electronics Engineering Yaşar University İzmir Turkey

this paper presents a learning Feedback Linearization (LFL) based Nonlinear Auto-Regressive Moving Average (NARMA) controller design for a ROTary inverted PENdulum (ROTPEN) plant. the proposed NARMA controller comprises of a linear controller and an LFL block. the LFL block concatenated with the nonlinear plant constitutes a linear closed loop system so that linear control is applicable. An online learning algorithm is used for the data-dependent identification of the linearized plant and then for the data-dependent design of the linear part of the NARMA controller. the identification of the linearized plant starts with the determination of the LFL block in a supervised way by exploiting the input and the corresponding state data obtained from the nonlinear plant. the linearized plant is then identified as an ARMA model by the data generated with the combination of the already learned LFL block and the nonlinear plant. Robustness of the linearized system model is obtained by employing the ε-insensitive loss function ℓ 1, ε (.,.) as the identification error of the linearized system. the Schur stability of the overall closed loop system is ensured by the linear inequality constraints imposed in the minimization of the ℓ 1, ε (,.) tracking error for determining the linear controller parameters. the proposed LFL based NARMA controller is tested on ROTPEN model and its performance is compared with the Proportional-Derivative controller and Hammerstein based NARMA adaptive controller.

关键词： Neural networks Mechatronics Process control conferences learning systems

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Weighted Voting and Meta-learning for Combining Authorship Attribution Methods 19th

Weighted Voting and Meta-Learning for Combining Authorship A...

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

作者： Petrovic, Smiljana Petrovic, Ivan Palesi, Ileana Calise, Anthony Iona Coll New Rochelle NY 10801 USA CUNY Bronx Community Coll Bronx NY 10453 USA

ISBN: (纸本)9783030034931;9783030034924

Our research concentrates on ways to combine machine learning techniques for authorship attribution. Traditionally, research in authorship attribution is focused on the development of new base-classifiers (combinations of stylometric features and learning methods). A large number of base-classifiers developed for authorship attribution vary in accuracy, often proposing different authors for a disputed document. In this research, we use predictions of multiple base-classifiers as a knowledge base for learning the true author. We introduce and compare two novel methods that utilize multiple base-classifiers. In the Weighted Voting approach, each base-classifier supports an author in proportion to its accuracy in leave-one-out classification. In our Meta-learning approach, each base-classifier is treated as a feature and methods' predictions in leave-one-out cross-validation are used as training data from which machine learning methods produce an aggregated decision. We illustrate our results through a collection of 18th century political writings. Anonymously written essays were common during this period, leading to frequent disagreements between scholars over their attribution.

关键词： Authorship attribution Combining classifiers Meta-learning

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