Resource Type

Journal Article 324

Conference Videos 4

Year

2023 64

2022 69

2021 39

2020 39

2019 25

2018 17

2017 22

2016 8

2015 11

2014 1

2013 1

2011 1

2010 1

2009 2

2008 1

2007 3

2006 1

2005 1

2001 3

open ︾

Keywords

Machine learning 50

Deep learning 36

machine learning 24

Reinforcement learning 15

deep learning 15

Artificial intelligence 14

artificial intelligence 5

Active learning 4

artificial neural network 4

Attention 3

Autonomous driving 3

Bayesian optimization 3

Big data 3

Adaptive dynamic programming 2

Additive manufacturing 2

Adversarial attack 2

Autonomous learning 2

Autonomous vehicle 2

Bayesian belief network 2

open ︾

Search scope:

排序： Display mode:

One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning Research Articles

Minggang DONG, Ming LIU, Chao JING,jingchao@glut.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 2,   Pages 278-290 doi: 10.1631/FITEE.2000417

Abstract: Since traditional machine learning methods are sensitive to skewed distribution and do not consider thecharacteristics in multiclass imbalance problems, the skewed distribution of multiclass data poses amajor challenge to machine learning algorithms.Finally, we collect 20 public real-world imbalanced data sets from the Knowledge Extraction based onEvolutionary Learning (KEEL) repository and the University of California, Irvine (UCI) repository.

Keywords： Decision trees     Multiclass imbalanced learning     Node splitting criterion     Hellinger distance     One-against-all    

HTML PDF Collect

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Frontiers of Mechanical Engineering 2021, Volume 16, Issue 4,   Pages 829-839 doi: 10.1007/s11465-021-0652-4

Abstract: However, the collection of fault signals is very difficult and expensive, resulting in the problem of imbalancedTo address this problem, an imbalanced fault diagnosis of rotating machinery using autoencoder-basedSuperGraph feature learning is proposed in this paper.On the basis, graph convolution is performed on the constructed SuperGraph to achieve imbalanced trainingtraining dataset through graph feature learning.

Keywords： imbalanced fault diagnosis     graph feature learning     rotating machinery     autoencoder    

HTML PDF Collect

Classifying multiclass relationships between ASes using graph convolutional network

Frontiers of Engineering Management   Pages 653-667 doi: 10.1007/s42524-022-0217-1

Abstract: differences between AS relationships under the structural and attribute features, and the reasons why multiclass

Keywords： autonomous system     multiclass relationship     graph convolutional network     classification algorithm     Internet    

HTML PDF Collect

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Frontiers of Information Technology & Electronic Engineering 2015, Volume 16, Issue 11,   Pages 930-939 doi: 10.1631/FITEE.1500125

Abstract: Head pose estimation has been considered an important and challenging task in computer vision. In this paper we propose a novel method to estimate head pose based on a deep convolutional neural network (DCNN) for 2D face images. We design an effective and simple method to roughly crop the face from the input image, maintaining the individual-relative facial features ratio. The method can be used in various poses. Then two convolutional neural networks are set up to train the head pose classifier and then compared with each other. The simpler one has six layers. It performs well on seven yaw poses but is somewhat unsatisfactory when mixed in two pitch poses. The other has eight layers and more pixels in input layers. It has better performance on more poses and more training samples. Before training the network, two reasonable strategies including shift and zoom are executed to prepare training samples. Finally, feature extraction filters are optimized together with the weight of the classification component through training, to minimize the classification error. Our method has been evaluated on the CAS-PEAL-R1, CMU PIE, and CUBIC FacePix databases. It has better performance than state-of-the-art methods for head pose estimation.

Keywords： Head pose estimation     Deep convolutional neural network     Multiclass classification    

HTML PDF Collect

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 6, doi: 10.1007/s11783-023-1677-1

Abstract:

● MSWNet was proposed to classify municipal solid waste.

Keywords： Municipal solid waste sorting     Deep residual network     Transfer learning     Cyclic learning rate     Visualization    

HTML PDF Collect

Spatial prediction of soil contamination based on machine learning: a review

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 8, doi: 10.1007/s11783-023-1693-1

Abstract:

● A review of machine learning (ML) for spatial prediction of soil

Keywords： Soil contamination     Machine learning     Prediction     Spatial distribution    

HTML PDF Collect

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Frontiers of Environmental Science & Engineering 2023, Volume 17, Issue 11, doi: 10.1007/s11783-023-1738-5

Abstract:

● A novel integrated machine learning method to analyze O3

Keywords： Ozone     Integrated method     Machine learning    

HTML PDF Collect

The modified Adaboost algorithm for Chinese handwritten character recognitionThe modified Adaboost algorithm for Chinese handwritten character recognition

Ding Xiaoqing,Fu Qiang

Strategic Study of CAE 2009, Volume 11, Issue 10,   Pages 19-24

Abstract:

The proposed modified Adaboost algorithm adopts the descriptive model based on multi-class classifiers (modified quadratic discriminant function, MQDF) as element classifiers which perform multi-class classification directly. It does not need to convert multi-class classifications to multiple binary classifications and has lower training complexity. Besides, it updates sample weights according to the generalized confidence which is simple and effective. In order to reduce the recognition complexity, the pruning method was performed to pick out only one best element classifier from all boosted classifiers to do the classification. Applying the algorithm to Chinese handwritten character recognition on HCL2000 and THOCR-HCD databases, the relative error rate reduced 14.3 %, 8.1 % and 19.5 % respectively.

Keywords： multiclass Adaboost algorithm     Chinese handwritten character recognition     generalized confidence     modified    

HTML PDF Collect

Machine learning in building energy management: A critical review and future directions

Frontiers of Engineering Management 2022, Volume 9, Issue 2,   Pages 239-256 doi: 10.1007/s42524-021-0181-1

Abstract: Over the past two decades, machine learning (ML) has elicited increasing attention in building energy

Keywords： building energy management     machine learning     integrated framework     knowledge evolution    

HTML PDF Collect

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 183-197 doi: 10.1007/s11705-021-2073-7

Abstract: exploration of the design variable space for such scenarios, an adaptive sampling technique based on machine learning

Keywords： machine learning     flowsheet simulations     constraints     exploration    

HTML PDF Collect

Machine learning for fault diagnosis of high-speed train traction systems: A review

Frontiers of Engineering Management doi: 10.1007/s42524-023-0256-2

Abstract: In recent years, machine learning has been widely used in various pattern recognition tasks and has demonstratedMachine learning has made considerably advancements in traction system fault diagnosis; however, a comprehensiveThis paper primarily aims to review the research and application of machine learning in the field ofThen, the research and application of machine learning in traction system fault diagnosis are comprehensivelydiagnosis under actual operating conditions are revealed, and the future research trends of machine learning

Keywords： high-speed train     traction systems     machine learning     fault diagnosis    

HTML PDF Collect

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Frontiers of Structural and Civil Engineering   Pages 994-1010 doi: 10.1007/s11709-023-0942-5

Abstract: Hence, a gated recurrent unit (GRU)-based deep learning framework is proposed herein to dynamically predictdecision support for moving trajectory control and serve as a foundation for the application of deep learning

Keywords： dynamic prediction     moving trajectory     pipe jacking     GRU     deep learning    

HTML PDF Collect

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Frontiers of Medicine 2023, Volume 17, Issue 4,   Pages 768-780 doi: 10.1007/s11684-023-0982-1

Abstract: illustrating the relationship between the phenotype and genotype of each HCM subtype by using machine learningMachine learning modeling based on personal whole-exome data identified 46 genes with mutation burden

Keywords： machine learning methods     hypertrophic cardiomyopathy     genetic risk    

HTML PDF Collect

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Frontiers of Mechanical Engineering 2022, Volume 17, Issue 2, doi: 10.1007/s11465-022-0673-7

Abstract: CNN (ACNN) for fault diagnosis, which can automatically tune its three key hyper parameters, namely, learningFirst, a new deep reinforcement learning (DRL) is developed, and it constructs an agent aiming at controllingACNN is also compared with other published machine learning (ML) and deep learning (DL) methods.

Keywords： deep reinforcement learning     hyper parameter optimization     convolutional neural network     fault diagnosis    

HTML PDF Collect

Automated synthesis of steady-state continuous processes using reinforcement learning

Frontiers of Chemical Science and Engineering 2022, Volume 16, Issue 2,   Pages 288-302 doi: 10.1007/s11705-021-2055-9

Abstract: The present work demonstrates how reinforcement learning can be used for automated flowsheet synthesis

Keywords： automated process synthesis     flowsheet synthesis     artificial intelligence     machine learning     reinforcementlearning    

HTML PDF Collect

Title Author Date Type Operation

One-against-all-based Hellinger distance decision tree for multiclass imbalanced learning

Minggang DONG, Ming LIU, Chao JING,jingchao@glut.edu.cn

Journal Article

Imbalanced fault diagnosis of rotating machinery using autoencoder-based SuperGraph feature learning

Journal Article

Classifying multiclass relationships between ASes using graph convolutional network

Journal Article

Multiclass classification based on a deep convolutional

Ying CAI,Meng-long YANG,Jun LI

Journal Article

MSWNet: A visual deep machine learning method adopting transfer learning based upon ResNet 50 for municipal

Journal Article

Spatial prediction of soil contamination based on machine learning: a review

Journal Article

Elucidate long-term changes of ozone in Shanghai based on an integrated machine learning method

Journal Article

The modified Adaboost algorithm for Chinese handwritten character recognitionThe modified Adaboost algorithm for Chinese handwritten character recognition

Ding Xiaoqing,Fu Qiang

Journal Article

Machine learning in building energy management: A critical review and future directions

Journal Article

Using machine learning models to explore the solution space of large nonlinear systems underlying flowsheet

Journal Article

Machine learning for fault diagnosis of high-speed train traction systems: A review

Journal Article

Dynamic prediction of moving trajectory in pipe jacking: GRU-based deep learning framework

Journal Article

Machine learning modeling identifies hypertrophic cardiomyopathy subtypes with genetic signature

Journal Article

A new automatic convolutional neural network based on deep reinforcement learning for fault diagnosis

Journal Article

Automated synthesis of steady-state continuous processes using reinforcement learning

Journal Article