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检索条件"任意字段=21th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2020"

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Workshop on Machine learning in Smart Mobility 21st

Workshop on Machine Learning in Smart Mobility

引用

21st international conference on intelligent data engineering and automated learning

作者： Ferreira, Sara Cardoso, Henrique Lopes Rossetti, Rosaldo J. F. Artificial Intelligence & Comp Sci Lab LIACC Porto Portugal Res Ctr Terr Transports & Environm CITTA Porto Portugal Univ Porto Fac Engn Porto Portugal

the workshop on Machine learning in Smart Mobility (MLSM) was co-located with the 21st international conference on intelligent data engineering and automated learning—ideal 2020, held online on November 4–6. © ... 详细信息

ISBN: (纸本)9783030623647;9783030623654

the workshop on Machine learning in Smart Mobility (MLSM) was co-located with the 21^st international conference on intelligent data engineering and automated learning—ideal 2020, held online on November 4–6. © 2020, Springer Nature Switzerland AG.

关键词： Machine learning

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On Random-Forest-Based Prediction Intervals 1

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21st international conference on intelligent data engineering and automated learning

作者： Calvino, Aida Univ Complutense Madrid Dept Stat & Data Sci Madrid Spain

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

In the context of predicting continuous variables, many proposals in the literature exist dealing with point predictions. However, these predictions have inherent errors which should be quantified. Prediction intervals (PI) are a great alternative to point predictions, as they permit measuring the uncertainty of the prediction. In this paper, we review Quantile Regression Forests and propose five new alternatives based on them, as well as on classical random forests and linear and quantile regression, for the computation of PIs. Moreover, we perform several numerical experiments to evaluate the performance of the reviewed and proposed methods and extract some guidelines on the method to choose depending on the size of the data set and the shape of the target variable.

关键词： Quantile Regression Coverage probability Skewness Kurtosis Machine learning

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Generalized Derivative Based Kernelized learning Vector Quantization

Generalized Derivative Based Kernelized Learning Vector Quan...

引用

11th international conference on intelligent data engineering and automated learning

作者： Schleif, Frank-Michael Villmann, thomas Hammer, Barbara Schneider, Petra Biehl, Michael Univ Bielefeld Dept Techn Univ Str 21-23 D-33615 Bielefeld Germany Univ Appl Faculty Math Natural & CS D-09648 Mittweida Germany Univ Groningen Johann Bernoulli Inst Math & CS NL-9700 Groningen Netherlands

ISBN: (纸本)9783642153808

We derive a novel derivative based version of kernelized Generalized learning Vector Quantization (KGLVQ) as an effective, easy to interpret, prototype based and kernelized classifier. It is called D-KGLVQ and we provide generalization error bounds, experimental results on real world data, showing that D-KGLVQ is competitive with KGLVQ and the SVM on UCI data and additionally show that automatic parameter adaptation for the used kernels simplifies the learning.

关键词： Vector quantization

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learning User Comfort and Well-Being through Smart Devices 21st

Learning User Comfort and Well-Being Through Smart Devices

引用

21st international conference on intelligent data engineering and automated learning

作者： Sousa, David Silva, Fabio Analide, Cesar Univ Minho ALGORITMI Ctr Dept Informat Braga Portugal Politecn Porto ESTG CIICESI Felgueiras Portugal

ISBN: (纸本)9783030623616;9783030623623

this article aims to provide a large-scale study, without geographical restrictions, on how people's habits can influence their comfort and well-being. In this sense, sensing techniques are used, through smart devices, such as the smartphone, with the main objective of collecting information about the user and the environment that surrounds him. the collected data are subsequently processed, and several models of deep learning are built that aim to predict the well-being and comfort in the different environments in which a person is inserted. However, due to the pandemic, the main focus has been changed and the main objective is to understand if it is possible to predict comfort and well-being in the quarantine.

关键词： Comfort Deep learning Supervised learning Well-being

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intelligent Call Routing for Telecommunications Call-Centers 21st

Intelligent Call Routing for Telecommunications Call-Centers

引用

21st international conference on intelligent data engineering and automated learning

作者： Jorge, Sergio Pereira, Carlos Novais, Paulo Univ Minho Campus Gualtar P-4710 Braga Portugal NOS Comunicacoes Senhora Da Hora Portugal Univ Minho Algoritmi Ctr Campus Gualtar P-4710 Braga Portugal

ISBN: (纸本)9783030623616;9783030623623

At telecommunications companies, call-centers have the highest interaction with customers, and the operators' performance is vital because an excellent service satisfies the customer and helps a better operation. therefore, attempts are made to use customer data, call operator data, and historical service data to improve support. Pairing a customer with an operator who is comfortable with the problem to solve helps companies reducing costs, improves customer service, and increases employee productivity. In this article, we propose an approach based on machine learning and optimization, which predicts the problem for which the customer is calling and routes the call and the customer to the most appropriate call operator. the results show that using large amounts of business data along with innovative algorithms such as LightGBM can improve the customer support performance.

关键词： Call-center Customer data mining Machine learning Optimization Telecommunications Workforce

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Instance-Based Stacked Generalization for Transfer learning 19th

Instance-Based Stacked Generalization for Transfer Learning

引用

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

作者： Baghoussi, Yassine Mendes-Moreira, Joao LIAAD INESC TEC Porto Portugal Univ Porto Fac Sci Porto Portugal Univ Porto Fac Engn Porto Portugal

ISBN: (纸本)9783030034931;9783030034924

We present a method for improving the prediction accuracy using multiple predictive algorithms. Several techniques have been developed to tackle this issue such as bagging, boosting and stacking. In contrary to the first two that, usually, generate homogeneous ensembles of classifiers, stacking techniques have demonstrated success using heterogeneous ensembles. In our method, we adopt the stacking mechanism. Several models are generated using different learning algorithms. Forward stepwise selection is implemented to link each instance to its appropriate learning model. Experiments with three datasets benchmarked with four learning schemes show that this novel method improves prediction accuracy and can serve as a bridge to transfer knowledge between tasks given the same feature space but different data distributions.

关键词： Ensemble learning Stacking Transfer learning Operational planning

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Improving Adversarial learning with Image Quality Measures for Image Deblurring 1

引用

21st international conference on intelligent data engineering and automated learning

作者： Su, Jingwen Yin, Hujun Univ Manchester Dept Elect & Elect Engn Manchester M13 9PL Lancs England

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

Generative adversarial networks (GANs) have become popular and powerful models for solving a wide range of image processing problems. We introduce a novel component based on image quality measures in the objective function of GANs for solving image deblurring problems. Such additional constraints can regularise the training and improve the performance. Experimental results demonstrate marked improvements on generated or restored image quality both quantitatively and visually. Boosted model performances are observed and testified on three test sets with four image quality measures. It shows that image quality measures are additional flexible, effective and efficient loss components to be adopted in the objective function of GANs.

关键词： Generative adversarial networks Image deblurring Image quality measures

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Using Deep learning for Ordinal Classification of Mobile Marketing User Conversion 1

引用

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

作者： Miguel Matos, Luis Cortez, Paulo Castro Mendes, Rui Moreau, Antoine Univ Minho ALGORITMI Ctr P-4804533 Guimaraes Portugal OLAmobile Spinpk P-4805017 Guimaraes Portugal

ISBN: (数字)9783030336073

ISBN: (纸本)9783030336073;9783030336066

In this paper, we explore Deep Multilayer Perceptrons (MLP) to perform an ordinal classification of mobile marketing conversion rate (CVR), allowing to measure the value of product sales when an user clicks an ad. As a case study, we consider big data provided by a global mobile marketing company. Several experiments were held, considering a rolling window validation, different datasets, learning methods and performance measures. Overall, competitive results were achieved by an online deep learning model, which is capable of producing real-time predictions.

关键词： Mobile performance marketing Multilayer perceptron Ordinal classification

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Using Kullback-Leibler Divergence to Identify Prominent Sensor data for Fault Diagnosis 1

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21st international conference on intelligent data engineering and automated learning

作者： Monteiro, Rodrigo P. Bastos-Filho, Carmelo J. A. Univ Fed Pernambuco Recife PE Brazil Univ Pernambuco Recife PE Brazil

ISBN: (数字)9783030623623

ISBN: (纸本)9783030623616;9783030623623

the combination of machine learning techniques and signal analysis is a well-known solution for the fault diagnosis of industrial equipment. Efficient maintenance management, safer operation, and economic gains are three examples of benefits achieved by using this combination to monitor the equipment condition. In this context, the selection of meaningful information to train machine learning models arises as an important issue, since it influences the model accuracy and complexity. Aware of this, we propose to use the ratio between the interclass and intraclass Kullback-Leibler divergence to identify promising data for training fault diagnosis models. We assessed the performance of this metric on compressor fault datasets. the results suggested a relation between the model accuracy and the ratio between the average interclass and intraclass divergences.

关键词： Deep learning Machine learning Kullback-Leibler divergence

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A Novel Ensemble Approach for Improving Generalization Ability of Neural Networks

A Novel Ensemble Approach for Improving Generalization Abili...

引用

9th international conference on intelligent data engineering and automated Learn

作者： Lu, Lei Zeng, Xiaoqin Wu, Shengli Zhong, Shuiming Hohai Univ Dept Comp Sci & Engn Nanjing Peoples R China Univ Ulster Sch Comp & Math Coleraine BT52 1SA Londonderry North Ireland

ISBN: (纸本)9783540889052

Ensemble learning is one of the main directions in machine learning and data mining, which allows learners to achieve higher training accuracy and better generalization ability. In this paper, with an aim at improving generalization performance, a novel approach to construct all ensemble of neural networks is proposed. the main contributions of the approach are its diversity measure for selecting diverse individual neural networks and weighted fusion technique for assigning proper weights to the selected individuals. Experimental results demonstrate that the proposed approach is effective.

关键词： Ensemble learning diversity sensitivity fusion clustering the second training

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