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

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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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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BLER performance evaluation of an enhanced channel autoencoder

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COMPUTER COMMUNICATIONS 2021年 176卷 173-181页

作者： Njoku, Judith Nkechinyere Morocho-Cayamcela, Manuel Eugenio Lim, Wansu Kumoh Natl Inst Technol Dept Aeronaut Mech & Elect Convergence Engn Gumi South Korea Yachay Tech Univ Sch Math & Computat Sci San Miguel De Urcuqui Ecuador

The concept of using autoencoders (AEs) to represent wireless communication systems as an end-to-end reconstruction task that optimizes the transmitter and receiver components simultaneously in a single process has attracted the attention of wireless practitioners worldwide. This is attributable to the flexibility, and convenience of representing complex channel models. However, owing to the characteristics of deep neural networks (DNNs), as the AE learns the representation of the channel, overfitting limits its performance. In this paper, we propose RegAE, a regularized DNN architecture that overcomes the overfitting limitation in AEs and reduces their training complexity, which are characteristics of models with higher dimensions. We demonstrate that RegAE improves the block error rate (BLER) as compared with equivalent models from the literature. Thereby, it achieves a performance (1) better than that of a 4/7 rate Hamming code with a 16 phase-shift keying (16PSK) modulation under an additive white Gaussian noise (AWGN) channel, (2) comparable to that of a 4/7 rate maximum likelihood decoding (MLD) with a E-b/N-0 range from 1 dB to 5 dB, and (3) equivalent to that of an uncoded binary phase-shift keying (BPSK) modulation over a E-b/N-0 range from 0 dB to 10 dB.

关键词： autoencoder Deep learning Neural network Wireless communications Modulation End-to-end learning

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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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Hybrid deep autoencoder network based adaptive cross guided bilateral filter for motion artifacts correction and denoising from MRI

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IMAGING SCIENCE JOURNAL 2024年第1期72卷 76-91页

作者： Samuel, Shiju Ochawar, Rohini S. S. Rukmini, M. S. S. Vignans Fdn Sci Technol & Res Dept Elect & Commun Engn Guntur Andhra Pradesh India Shri Ramdeobaba Coll Engn & Management Dept Elect Engn Nagpur Maharashtra India

Removal of motion artifacts from medical images is essential, because the respiration by patient causes degradation in artifact removal. Therefore, getting an MRI without any artifacts and noise is challenging. Previous techniques failed to preserve the image quality during denoising, thereby affecting the classification accuracy. To overcome such challenges, two modules are considered in this work: classification and denoising. The classification process is successfully performed by Hybrid Deep autoencoder-convolutional neural network (DAE-CNN). It classifies the original images into motion artifacts, available and normal images. Then, the available artifacts are removed using cross guided bilateral filter (CGBF). The optimal parameters required to improve the CGBF performance are selected using the hybrid optimization algorithm. The filter's performance relies on the optimal spatial and range kernels selection. Finally, the classification and denoising-based results are evaluated using the BRATS 2015 dataset and achieved 95.84% accuracy and PSNR - 46.4, which is better than existing methods.

关键词： Salp swarm optimization Motion artifact removal Deep learning cross-guided bilateral filtering Peak Signal to Noise Ratio (PSNR) bald eagle search optimization autoencoder kernel selection

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High-Ratio Lossy Compression: Exploring the autoencoder to Compress Scientific Data

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IEEE TRANSACTIONS ON BIG DATA 2023年第1期9卷 22-36页

作者： Liu, Tong Wang, Jinzhen Liu, Qing Alibhai, Shakeel Lu, Tao He, Xubin Temple Univ Dept Comp & Informat Sci Philadelphia PA 19122 USA New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA

Scientific simulations on high-performance computing (HPC) systems can generate large amounts of floating-point data per run. To mitigate the data storage bottleneck and lower the data volume, it is common for floating-point compressors to be employed. As compared to lossless compressors, lossy compressors, such as SZ and ZFP, can reduce data volume more aggressively while maintaining the usefulness of the data. However, a reduction ratio of more than two orders of magnitude is almost impossible without seriously distorting the data. In deep learning, the autoencoder technique has shown great potential for data compression, in particular with images. Whether the autoencoder can deliver similar performance on scientific data, however, is unknown. In this article, we for the first time conduct a comprehensive study on the use of autoencoders to compress real-world scientific data and illustrate several key findings on using autoencoders for scientific data reduction. We implement an autoencoder-based compression prototype to reduce floating-point data. Our study shows that the out-of-the-box implementation needs to be further tuned in order to achieve high compression ratios and satisfactory error bounds. Our evaluation results show that, for most of the test datasets, the tuned autoencoder outperforms SZ by up to 4X, and ZFP by up to 50X in compression ratios, respectively. Our practices and lessons learned in this work can direct future optimizations for using autoencoders to compress scientific data.

关键词： Compressors Image coding Prototypes Big Data Decoding Data models Tuning Lossy data compression autoencoder machine learning scientific data

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Deep autoencoder for false positive reduction in handgun detection

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NEURAL COMPUTING & APPLICATIONS 2021年第11期33卷 5885-5895页

作者： Vallez, Noelia Velasco-Mata, Alberto Deniz, Oscar Univ Castilla La Mancha ETSI Ind VISILAB Ave Camilo Jose Cela SN Ciudad Real 13071 Spain

In an object detection system, the main objective during training is to maintain the detection and false positive rates under acceptable levels when the model is run over the test set. However, this typically translates into an unacceptable rate of false alarms when the system is deployed in a real surveillance scenario. To deal with this situation, which often leads to system shutdown, we propose to add a filter step to discard part of the new false positive detections that are typical of the new scenario. This step consists of a deep autoencoder trained with the false alarm detections generated after running the detector over a period of time in the new scenario. Therefore, this step will be in charge of determining whether the detection is a typical false alarm of that scenario or whether it is something anomalous for the autoencoder and, therefore, a true detection. In order to decide whether a detection must be filtered, three different approaches have been tested. The first one uses the autoencoder reconstruction error measured with the mean squared error to make the decision. The other two use thek-NN (k-nearest neighbors) and one-class SVMs (support vector machines) classifiers trained with the autoencoder vector representation. In addition, a synthetic scenario has been generated with Unreal Engine 4 to test the proposed methods in addition to a dataset with real images. The results obtained show a reduction in the number of false positives between 22.5% and 87.2% and an increase in the system's precision of 1.2%-47% when the autoencoder is applied.

关键词： Handgun detection False positive reduction autoencoder One-class classification

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A Twin Learning Framework for Traveling Salesman Problem Based on autoencoder, Graph Filter, and Transfer Learning

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IEEE TRANSACTIONS ON CONSUMER ELECTRONICS 2024年第1期70卷 3245-3258页

作者： Wu, Junjun Yang, Hanshi Zeng, Yan Wu, Zhou Liu, Jiepeng Feng, Liang Chongqing Univ Sch Automat Chongqing 400044 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400044 Peoples R China Chongqing Univ Sch Comp Sci Chongqing 400044 Peoples R China

Efficient solvers for traveling salesman problem (TSP) have great significance in the field of consumer electronic systems and devices. Existing studies require independent and repetitive runs for similar TSPs. To utilize useful knowledge buried in the twin TSP which is similar to the target TSP, a twin learning framework based on task matching and mapping strategy is proposed. We use an autoencoder to extract the feature vectors of the historical tasks and the target task to find the twin task. If not found, construct a twin task based on a graph-filter. Further, we get the solution of the target task with the optimized twin task by learning a mapping matrix from the twin task to the target task. Finally, we incorporate local search algorithms into the twin learning framework, which called strategy mapping solver (SMS) to further improve the quality of the solution. The efficacy of the SMS is evaluated through comprehensive empirical studies with commonly used TSP benchmarks, against meta-heuristic algorithms and a learning improvement heuristics algorithm, demonstrating its effectiveness for TSP. Moreover, a real-world combinatorial optimization application, laser engraving path planning, is presented to further confirm the efficacy of SMS.

关键词： Task analysis Urban areas Optimization Heuristic algorithms Metaheuristics Statistics Sociology Traveling salesman problem twin learning autoencoder graph-filter path planning

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