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Feature Learning With a Divergence-Encouraging autoencoder for Imbalanced Data Classification

IEEE ACCESS 2018年 6卷 70197-70211页

作者： Luo, Ruisen Feng, Qian Wang, Chen Yang, Xiaomei Tu, Haiyan Yu, Qin Fei, Shaomin Gong, Xiaofeng Sichuan Univ Coll Elect Engn & Informat Technol Chengdu 610064 Sichuan Peoples R China UCL Dept Comp Sci London WC1E 6BT England Chengdu Univ Informat Technol Expt Ctr Elect Chengdu 610059 Sichuan Peoples R China DaGongBoChuang Corp Chengdu 610005 Sichuan Peoples R China

Imbalanced data exists commonly in machine learning classification applications. Popular classification algorithms are based on the assumption that data in different classes are roughly equally distributed;however, extremely skewed data, with instances from one class taking up most of the dataset, is not exceptional in practice. Thus, performance of algorithm often degrades significantly when encountering skewed data. Mitigating the problem caused by imbalanced data has been an open challenge for years, and previous researches mostly have proposed solutions from the perspectives of data re-sampling and algorithm improvement. In this paper, focusing on two-class imbalanced data, we have proposed a novel divergence-encouraging autoencoder (DEA) to explicitly learn features from both of the two classes and have designed an imbalanced data classification algorithm based on the proposed autoencoder. By encouraging maximization of divergence loss between different classes in the bottleneck layer, the proposed DEA can learn features for both majority and minority classes simultaneously. The training procedure of the proposed autoencoder is to alternately optimize reconstruction and divergence losses. After obtaining the features, we directly compute the cosine distances between the training and testing features and compare the median of distances between classes to perform classification. Experimental results illustrate that our algorithm outperform ordinary and loss-sensitive CNN models both in terms of performance evaluation metrics and convergence properties. To the best of our knowledge, this is the first paper proposed to solve the imbalanced data classification problem from the perspective of explicitly learning representations of different classes simultaneously. In addition, designing of the proposed DEA is also an innovative work, which could improve the performance of imbalanced data classification without data re-sampling and benefit future researches in the fie

关键词： Imbalanced data classification autoencoder divergence loss convergence analysis alternating training paradigm

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Effectiveness of autoencoder for lake area extraction from high-resolution RGB imagery: an experimental study

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ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH 2021年第24期28卷 31084-31096页

作者： Tercan, Emre Atasever, Umit Haluk Gen Directorate Highways Dept Survey Project & Environm 13th Reg TR-07090 Antalya Turkey Erciyes Univ Fac Engn Dept Geomat Engn TR-38039 Kayseri Turkey

The surface areas of lakes alter constantly due to many factors such as climate change, land use policies, and human interventions, and their surface areas tend to decrease. It is necessary for obtain baseline datasets such as surface areas and boundaries of water bodies with high accuracy, effectively, economically, and practically by using satellite images in terms of management and planning of lakes. Extracting surface areas of water bodies using image classification algorithms and high-resolution RGB satellite images and evaluating the effectiveness of different image classification algorithms have become an important research domain. In this experimental study, eight different machine learning-based classification approaches, namely, k-nearest neighborhood (kNN), subspaced kNN, support vector machines (SVMs), random forest (RF), bagged tree (BT), Naive Bayes (NB), and linear discriminant (LD), have been utilized to extract the surface areas of lakes. Lastly, autoencoder (AE) classification algorithm was applied, and the effectiveness of all those algorithms was compared. Experimental studies were carried out on three different lakes (Hazar Lake, Salda Lake, Manyas Lake) using high-resolution Turkish RASAT RGB satellite images. The results indicated that AE algorithm obtained the highest accuracy values in both quantitative and qualitative analyses. Another important aspect of this study is that Structural Similarity Index (SSIM) and Universal Image Quality Index (UIQI) metrics that can evaluate close to human perception are used for comparison. With this application, it has been shown that overall accuracy calculated from test data may be inadequate in some cases by using SSIM, UIQI, mean squared error (MSE), peak signal to noise ratio (PSNR), and Cohen's KAPPA metrics. In the last application, the robustness of AE was examined with boxplots.

关键词： Machine learning autoencoder Remote sensing Image classification Water body extraction RASAT Environmental monitoring Lake management

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BSSNet: A Real-Time Semantic Segmentation Network for Road Scenes Inspired From autoencoder

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2024年第5期34卷 3424-3438页

作者： Shi, Xiaoqiang Yin, Zhenyu Han, Guangjie Liu, Wenzhuo Qin, Li Bi, Yuanguo Li, Shurui Univ Chinese Acad Sci Sch Comp Sci & Technol Beijing 100049 Peoples R China Chinese Acad Sci Shenyang Inst Comp Technol Shenyang 110168 Peoples R China Liaoning Key Lab Domest Ind Control Platform Techn Shenyang 110168 Peoples R China Hohai Univ Dept Internet Things Engn Changzhou 213022 Peoples R China China Univ Min & Technol Beijing Sch Artificial Intelligence Beijing 100083 Peoples R China Northeastern Univ Sch Comp Sci & Engn Shenyang 110167 Peoples R China Shenyang Aerosp Univ Sch Comp Sci Shenyang 110136 Peoples R China

Although semantic segmentation methods have made remarkable progress so far, their long inference process limits their use in practical applications. Recently, some two-branch and three-branch real-time segmentation networks have been proposed to improve segmentation accuracy by adding branches to extract spatial or border information. For the design of extracting spatial information branches, preserving high-resolution features or adding segmentation loss to guide spatial branches are commonly used methods to extract spatial information. However, these approaches are not the most efficient. To solve the problem, we design the spatial information extraction branch as an autoencoder structure, which allows us to extract the spatial structure and features of the image during the encoding and decoding process of the autoencoder. Border, semantic and spatial information are all helpful for segmentation tasks, and efficiently fusing these three kinds of information can obtain better feature representation compared to the fusion of two types of information in the dual-branch network. However, existing three-branch networks have yet to explore this aspect deeply. Therefore, this paper designs a new three-branch network based on this starting point. In addition, we also propose a feature fusion module called the Unified Multi-Feature Fusion module (UMF), which can fuse multiple features efficiently. Our method achieves a state-of-the-art trade-off between inference speed and accuracy on the Cityscapes, CamVid, and NightCity datasets. Specifically, BSSNet-T achieves 78.8% mIoU at 115.8 FPS on the Cityscapes dataset, 79.5% mIoU at 170.8 FPS on the CamVid dataset, and 52.6% mIoU at 172.3 FPS on the NightCity dataset. Code is available at https://***/SXQ-STUDY/BSSNet.

关键词： Real-time semantic segmentation convolution neural networks autoencoder feature fusion

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Densely connected convolutional network block based autoencoder for panorama map compression

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 80卷 115678-000页

作者： Wang, Shengwei Wang, Hongkui Xiang, Sen Yu, Li Huazhong Univ Sci & Technol Sch Electron Inform & Commun Wuhan 430074 Hubei Peoples R China Wuhan Univ Sci & Tech Sch Inform Sci & Engn Wuhan 430081 Hubei Peoples R China

As a novel virtual reality (VR) format, panorama maps are attracting increasing attention, while the compression of panorama images is still a concern. In this paper, a densely connected convolutional network block (dense block) based autoencoder is proposed to compress panorama maps. In the proposed autoencoder, dense blocks are specially designed to reuse feature maps and reduce redundancy of features. Meanwhile, a loss function, which imports a position-dependent weight item for each pixel, is proposed to train and adjust network parameters, in order to make the autoencoder fit to properties of panorama maps. Based on the proposed autoencoder and the weighted loss function, a greedy block-wise training scheme is also designed to avoid gradient vanishing problem and speed up training. During training process, the autoencoder is divided into several sub-nets. After each sub-net is trained separately, the whole network is fine-tuned to achieve the best performance. Experimental results demonstrate that the proposed autoencoder, compared with JPEG, saves up to 79.69 % bit rates, and obtains 7.27dB gain in BD-WS-PSNR or 0.0789 gain in BD-WS-SSIM. The proposed autoencoder also outperforms JPEG 2000, HEVC and VVC in both BD-WS-PSNR and BD-WS-SSIM. Meanwhile, subjective results show that the proposed autoencoder can recover details of panorama images, and reconstruct maps with high visual quality.

关键词： autoencoder Dense block Neural network Panorama map

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An autoencoder enhanced light gradient boosting machine method for credit card fraud detection

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PEERJ COMPUTER SCIENCE 2024年 10卷 e2323-e2323页

作者： Ding, Lianhong Liu, Luqi Wang, Yangchuan Shi, Peng Yu, Jianye Beijing Wuzi Univ Beijing Peoples R China Univ Sci & Technol Beijing Beijing Peoples R China

Online financial transactions bring convenience to people's lives, but also present vulnerabilities for criminals to embezzle users' accounts and trick users into credit card fraud. Although machine learning methods have been adopted to detect anomalous transactions, it's hard for a single machine learning method to achieve satisfying results with the increasing scale and dimensionality of financial datasets. In addition, for anomaly detection of financial data, there is an obvious imbalance between normal records and abnormal. In this situation, the experimental results cannot be objectively evaluated only by the traditional metrics, such as precision, recall, and accuracy. This paper proposes an autoencoder enhanced LightGBM method for credit card detection. The method inherits the advantages of each component, using an autoencoder for feature reconstruction on the dataset, and integrating the LightGBM algorithm for improving the GBDT (Gradient Boosting Decison Tree) to detect abnormal data more accurately and efficiently. Besides the traditional evaluation metrics, F-measure, area under curve (AUC), Matthew's correlation coefficient (MCC), and balanced classification rate (BCR) are also adopted as the evaluation metrics. Two financial datasets were used to validate the performance and robustness of the proposed model. Results obtained from the credit card fraud dataset containing 31 features indicate that our model significantly outperforms other models with a recall of 94.85%, representing a 10.70% improvement compared to the best detection performance model with a recall of only 86%. Additionally, our model's BCR score is also significantly better than other models, with a BCR score of 97%, as opposed to the best detection performance model's BCR score of 92%, representing a 5% improvement by our model. Various sampling methods and model combinations were considered in this study. It was found that the SMOTE algorithm combined with the proposed model produced t

关键词： Anomaly detection autoencoder BCR Credit card fraud LightGBM MCC

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A digital twin framework for anomaly detection in industrial robot system based on multiple physics-informed hybrid convolutional autoencoder

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JOURNAL OF MANUFACTURING SYSTEMS 2024年 77卷 798-809页

作者： Wang, Shijie Tao, Jianfeng Jiang, Qincheng Chen, Wei Qin, Chengjin Liu, Chengliang Shang Hai Jiao Tong Univ State Key Lab Mech Syst & Vibrat Shanghai 201100 Peoples R China Shanghai STEP Robot Co Ltd Shanghai 201800 Peoples R China

The robot number in industry is growing up rapidly. Building anomaly detection system for them can improve the security of these expensive devices. The article implements an anomaly detection framework based on digital twin, which are built by a hybrid convolutional autoencoder. The framework shares those neural network weight files as digital assets, users can use them to estimate the possible output from real input. It approximates the dynamic relationship between motion, current, temperature and vibration with hybrid convolution. Considering the limited generalization performance of direct data-driven methods in practical physical systems, this article introduces physical information methods to improve the constraint function of neural network. The influence of multiple physical fields on current is established by a unified neural network. Terminals detect anomaly with KL divergence between really current and estimated current. The article collects operational data from real robots and verifies it, and the experiment shows that the RMSE for current estimation is below 1.5 %, the F1-score in anomaly detection is over 98.23 %, false positive is below 1 %, false negative is below 1.7 %. The relevant technologies are gradually being promoted and applied in enterprises.

关键词： Anomaly detection Digital twin Industrial robot Physics-Informed autoencoder

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Perturbation of deep autoencoder weights for model compression and classification of tabular data

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NEURAL NETWORKS 2022年 156卷 160-169页

作者： Abrar, Sakib Samad, Manar D. Tennessee State Univ Dept Comp Sci Nashville TN 37209 USA Tennessee State Univ Dept Comp Sci 3500 John A Merritt BlvdPOB 9604 Nashville TN 37209 USA

Fully connected deep neural networks (DNN) often include redundant weights leading to overfitting and high memory requirements. Additionally, in tabular data classification, DNNs are challenged by the often superior performance of traditional machine learning models. This paper proposes periodic perturbations (prune and regrow) of DNN weights, especially at the self-supervised pretraining stage of deep autoencoders. The proposed weight perturbation strategy outperforms dropout learning or weight regularization (L1 or L2) for four out of six tabular data sets in downstream classification tasks. Unlike dropout learning, the proposed weight perturbation routine additionally achieves 15% to 40% sparsity across six tabular data sets, resulting in compressed pretrained models. The proposed pretrained model compression improves the accuracy of downstream classification, unlike traditional weight pruning methods that trade off performance for model compression. Our experiments reveal that a pretrained deep autoencoder with weight perturbation can outperform traditional machine learning in tabular data classification, whereas baseline fully-connected DNNs yield the worst classification accuracy. However, traditional machine learning models are superior to any deep model when a tabular data set contains uncorrelated variables. Therefore, the performance of deep models with tabular data is contingent on the types and statistics of constituent variables.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Weight pruning autoencoder Tabular data Model compression Sparse learning Deep neural network

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RUL Prediction of Rolling Bearings Based on Multi-Information Fusion and autoencoder Modeling

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PROCESSES 2024年第9期12卷 1831页

作者： Guan, Peng Zhang, Tianrui Zhou, Lianhong Shenyang Univ Sch Mech Engn Shenyang 110044 Peoples R China

As an important part of industrial equipment, the safe and stable operation of rolling bearings is an important guarantee for the performance of mechanical equipment. Aiming at the problem that it is difficult to characterize the running state of rolling bearings, this paper mainly analyzes and processes the vibration signals of rolling bearings, extracts and fuses multi-information entropy, and monitors the running state of rolling bearings and predicts the remaining useful life prediction (RUL) through test verification. Firstly, in view of the difficulty in characterizing the bearings running state characteristics, a rolling bearings running state monitoring method based on multi-information entropy fusion and denoising autoencoder (DAE) was proposed to extract the multi-entropy index features of vibration signals to improve the accuracy of feature extraction, and to solve the problem of not obvious information representation of a single feature indicator and missing information in the feature screening process. Secondly, in view of the problems of low prediction accuracy and poor robustness and generalization in traditional RUL models, a rolling bearings RUL model combining convolutional autoencoder (CAE) and bidirectional long short-term memory network (BiLSTM) was proposed. The introduction of convolution operation made CAE have the feature of weight sharing, reducing the complexity of the model. Finally, the XJTU-SY data set was used to verify the constructed model. The results show that the condition monitoring model established in this paper can accurately evaluate the running state of the rolling bearing and accurately locate the failure time. At the same time, the residual life prediction model can realize the residual life prediction of most data sets, and has good accuracy and robustness.

关键词： rolling bearings remaining useful life prediction autoencoder entropy feature

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ConvAE-LSTM: Convolutional autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition

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IEEE ACCESS 2022年 10卷 4137-4156页

作者： Thakur, Dipanwita Biswas, Suparna Ho, Edmond S. L. Chattopadhyay, Samiran Banasthali Vidyapith Dept Comp Sci Jaipur 304022 Rajasthan India Maulana Abul Kalam Azad Univ Technol Dept Comp Sci & Engn Kolkata 700064 W Bengal India Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England TCG Ctr Res & Educ Sci & Technol Inst Adv Intelligence Kolkata 700091 India Jadavpur Univ Dept Informat Technol Kolkata 700032 India

The self-regulated recognition of human activities from time-series smartphone sensor data is a growing research area in smart and intelligent health care. Deep learning (DL) approaches have exhibited improvements over traditional machine learning (ML) models in various domains, including human activity recognition (HAR). Several issues are involved with traditional ML approaches;these include handcrafted feature extraction, which is a tedious and complex task involving expert domain knowledge, and the use of a separate dimensionality reduction module to overcome overfitting problems and hence provide model generalization. In this article, we propose a DL-based approach for activity recognition with smartphone sensor data, i.e., accelerometer and gyroscope data. Convolutional neural networks (CNNs), autoencoders (AEs), and long short-term memory (LSTM) possess complementary modeling capabilities, as CNNs are good at automatic feature extraction, AEs are used for dimensionality reduction and LSTMs are adept at temporal modeling. In this study, we take advantage of the complementarity of CNNs, AEs, and LSTMs by combining them into a unified architecture. We explore the proposed architecture, namely, "ConvAE-LSTM", on four different standard public datasets (WISDM, UCI, PAMAP2, and OPPORTUNITY). The experimental results indicate that our novel approach is practical and provides relative smartphone-based HAR solution performance improvements in terms of computational time, accuracy, F1-score, precision, and recall over existing state-of-the-art methods.

关键词： Feature extraction Convolution Activity recognition Computer architecture Convolutional neural networks Older adults Monitoring Deep Learning human activity recognition smartphone sensors autoencoder

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Image Clustering: Utilizing Teacher-Student Model and autoencoder

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IEEE ACCESS 2023年 11卷 104846-104857页

作者： Fei, Zhengshun Gong, Haibo Guo, Junhao Wang, Jinglong Jin, Wuyin Xiang, Xinjian Ding, Xiasheng Zhang, Ni Lanzhou Univ Technol Sch Mech & Elect Engn Lanzhou 730050 Peoples R China Zhejiang Univ Sci & Technol Prov Key Inst Robot Sch Automat & Elect Engn Hangzhou 310023 Peoples R China Zhejiang Chenlong Sawing Machine Co Ltd Lishui 321404 Peoples R China

Complete and high-quality labeled dataset is indispensable for image classification. Considering the often arduous task of data labeling, clustering algorithms are commonly utilized in the preliminary stages of data classification to perform preliminary categorization. Traditional clustering algorithms, such as K-means and Gaussian Mixture Model, often struggle to effectively cluster images. In this paper, we propose a novel image clustering method utilizing teacher-student model and autoencoder. Specifically, the teacher model is a fully connected autoencoder and the student model consists of multiple convolutional autoencoders. We firstly obtain sub-datasets by applying the K-means algorithm to cluster the output of the teacher model, then utilize the student model to achieve clustering through an iterative data exchange method where the same data can converge. In addition, the proposed method can filter low-quality data to a certain extent by recording the frequency of exchanges because clustering is achieved during multiple exchanges of data. Experiments on a synthetic dataset and three benchmark image datasets (MNIST, FashionMNIST and USPS) show that our method can achieve satisfactory clustering results.

关键词： Image clustering Teacher-student model autoencoder

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