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A Parallel High-Utility Itemset Mining Algorithm Based on Hadoop

Complex System Modeling and Simulation 2023年第1期3卷 47-58页

作者： Zaihe Cheng Wei Shen Wei Fang Jerry Chun-Wei Lin School of Internet of Things Wuxi Institute of TechnologyWuxi 214121China School of Artificial Intelligence and Computer Science Jiangnan UniversityWuxi 214122China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan UniversityWuxi 214122China Department of Computer Science Electrical Engineering and Mathematical SciencesWestern Norway University of Applied SciencesBergen 5020Norway

High-utility itemset mining(HUIM)can consider not only the profit factor but also the profitable factor,which is an essential task in data ***,most HUIM algorithms are mainly developed on a single machine,which is inefficient for big data since limited memory and processing capacities are available.A parallel efficient high-utility itemset mining(P-EFIM)algorithm is proposed based on the Hadoop platform to solve this problem in this *** P-EFIM,the transaction-weighted utilization values are calculated and ordered for the itemsets with the MapReduce *** the ordered itemsets are renumbered,and the low-utility itemsets are pruned to improve the dataset *** the Map phase,the P-EFIM algorithm divides the task into multiple independent *** uses the proposed S-style distribution strategy to distribute the subtasks evenly across all nodes to ensure ***,the P-EFIM uses the EFIM algorithm to mine each subtask dataset to enhance the performance in the Reduce *** are performed on eight datasets,and the results show that the runtime performance of P-EFIM is significantly higher than that of the PHUI-Growth,which is also HUIM algorithm based on the Hadoop framework.

关键词： pattern mining data mining Hadoop parallel high-utility itemset mining big data

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A Many-Objective Evolutionary Algorithm Based on New Angle Penalized Distance

A Many-Objective Evolutionary Algorithm Based on New Angle P...

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Congress on Evolutionary Computation

作者： Junchao Fang Wei Fang Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

In evolutionary many-objective optimization, achieving better balance between convergence and diversity of the population is a crucial way to improve the efficiency of the algorithm. However, diversity measure may select the individuals having good diversity but degrade the convergence process to a certain extent. If the convergence measure focuses on the convergence of the individuals too much, it may lead to local convergence. The selection pressure achieves a severe loss, especially when the Pareto dominance selection mechanism is difficult to select solutions. To address these issues, a many-objective evolutionary algorithm based on new angle penalized distance is proposed in this paper, which is termed MaOEA-NAPD. In MaOEA-NAPD, it could dynamically balance the convergence and diversity of the population concerning their importance degree during the evolutionary process based on new angle penalized distance. In order to enhance the selection probability of better solutions in the mating pool, new convergence measure and diversity measure are introduced according to the achievement scalarizing function and angle based crowding degree estimation, respectively. The performance of the proposed method is evaluated and compared with five state-of-the-art algorithms on the WFG test suites with up to 15 objectives. Experimental results show the superior performance of MaOEA-NAPD than the compared algorithms on all the considered test instances.

关键词： Heuristic algorithms Sociology Estimation Evolutionary computation Statistics Optimization Convergence

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An Efficient and Flexible Automatic Search Algorithm for Convolution Network Architectures

An Efficient and Flexible Automatic Search Algorithm for Con...

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Congress on Evolutionary Computation

作者： Liang Zhao Wei Fang Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

As many superior convolutional neural networks (CNNs) have been proposed in recent years, CNNs have played an important role in computer vision. However, manually-designing CNN architecture is difficult since expertise is required. Therefore, several automatic search algorithms have been proposed for neural architecture search, which usually have considerable computational complexity and the search space is limited. To address these problems, an efficient and flexible CNN architecture search algorithm (EF-CNN) is proposed in this paper. In EF-CNN, a flexible architecture search space is constructed by considering the depth, width, and lightweight blocks. In order to improve the reliability of the architecture while reducing the computational time, a multi-objective fitness correction method is proposed in EF-CNN based on the divided datasets, where the accuracy and computational complexity of architecture are considered simultaneously to design CNN. The experimental results on CIFAR-10 and CIFAR-100 indicate that the performance of CNN architecture designed by EF-CNN is very competitive while the computational time is greatly reduced.

关键词： Computer vision Convolution Computer architecture Manuals Evolutionary computation Network architecture Search problems

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A Dual-Branch Network for Infrared and Visible Image Fusion

A Dual-Branch Network for Infrared and Visible Image Fusion

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International Conference on pattern recognition

作者： Yu Fu Xiao-Jun Wu Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

In recent years, deep learning has been used extensively in the field of image fusion. In this article, we propose a new image fusion method by designing a new structure and a new loss function for a deep learning model. Our backbone network is an autoencoder, in which the encoder has a dual branch structure. We input infrared images and visible light images to the encoder to extract detailed information and semantic information respectively. The fusion layer fuses two sets of features to get fused features. The decoder reconstructs the fusion features to obtain the fused image. We design a new loss function to reconstruct the image effectively. Experiments show that our proposed method achieves state-of-the-art performance.

关键词： Deep learning Fuses Image synthesis Semantics Feature extraction Decoding Data mining

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Spectral Efficiency for Multi-pair Massive MIMO Two-Way Relay Networks with Hybrid Processing 8th

Spectral Efficiency for Multi-pair Massive MIMO Two-Way Rela...

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8th International Conference on Communications, Signal Processing, and Systems, CSPS 2019

作者： Wang, Hongyan Li, Zhengquan Xue, Xiaomei Li, Baolong Liu, Yang Wu, Guilu Wu, Qiong Graduate School of Engineering Nagasaki University Nagasaki852-8521 Japan Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China National Mobile Communication Research Laboratory Southeast University Nanjing210096 China

ISBN: (纸本)9789811394089

In millimeter wave (mm-wave) communication systems, large antenna array can be employed for higher data rates. Hybrid digital and analogue beamforming design is adopted to reduce the digital signal processing (DSP) power consumption and the circuitry complexity. In this paper, we investigate the multi-pair massive MIMO two-way relay networks with hybrid processing architecture. Furthermore, the power scaling scheme is also considered. We got some conclusions that when the number of antennas at relay station M goes huge, the asymptotic spectral efficiency can be obtained. We also compared three cases of power scaling schemes and obtained the optimal case specially when M tends to considerable large. We can draw the conclusion that when, the Case 2 (only the transmit power of users scaled by M in power scaling schemes) is the optimal case compared with the other two cases. Finally, the Monte-Carlo is employed to demonstrate the validity of analytical results. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Spectrum efficiency

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Editorial

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International Journal of Bio-Inspired Computation 2020年第2期16卷 67-67页

作者： Fang, Wei Wu, Xiaojun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Department of Computer Science and Technology Jiangnan University 214122 China

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A dual-branch network for infrared and visible image fusion

arXiv

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arXiv 2021年

作者： Fu, Yu Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China

关键词： Image fusion

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Infrared and visible image fusion using Latent Low-Rank Representation

arXiv

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arXiv 2018年

作者： Li, Hui Wu, Xiao-Jun Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence School of IoT engineering Jiangnan University Wuxi China

Infrared and visible image fusion is an important problem in the field of image fusion which has been applied widely in many fields. To better preserve the useful information from source images, in this paper, we propose a novel image fusion method based on latent low-rank representation(LatLRR) which is simple and effective. Firstly, the source images are decomposed into low-rank parts(global structure) and salient parts(local structure) by LatLRR. Then, the low-rank parts are fused by weighted-average strategy to preserve more contour information. Then, the salient parts are simply fused by sum strategy which is a efficient operation in this fusion framework. Finally, the fused image is obtained by combining the fused low-rank part and the fused salient part. Compared with other fusion methods experimentally, the proposed method has better fusion performance than state-of-the-art fusion methods in both subjective and objective evaluation. The Code of our fusion method is available at https://***/hli1221/imagefusion_Infrared_visible_latlrr. Copyright © 2018, The Authors. All rights reserved.

关键词： Infrared imaging

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DreamNet: A Deep Riemannian Manifold Network for SPD Matrix Learning 16th

DreamNet: A Deep Riemannian Manifold Network for SPD Matrix...

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16th Asian Conference on Computer Vision, ACCV 2022

作者： Wang, Rui Wu, Xiao-Jun Chen, Ziheng Xu, Tianyang Kittler, Josef School of Artificial Intelligence and Computer Science Jiangnan University Wuxi214122 China Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi214122 China University of Surrey GuildfordGU2 7XH United Kingdom

ISBN: (纸本)9783031263507

The methods of symmetric positive definite (SPD) matrix learning have attracted considerable attention in many pattern recognition tasks, as they are eligible to capture and learn appropriate statistical features while respecting the Riemannian geometry of SPD manifold where the data reside on. Accompanied with the advanced deep learning techniques, several Riemannian networks (RiemNets) for SPD matrix nonlinear processing have recently been studied. However, it is pertinent to ask, whether greater accuracy gains can be realized by simply increasing the depth of RiemNets. The answer appears to be negative, as deeper RiemNets may be difficult to train. To explore a possible solution to this issue, we propose a new architecture for SPD matrix learning. Specifically, to enrich the deep representations, we build a stacked Riemannian autoencoder (SRAE) on the tail of the backbone network, i.e., SPDNet [23]. With this design, the associated reconstruction error term can prompt the embedding functions of both SRAE and of each RAE to approach an identity mapping, which helps to prevent the degradation of statistical information. Then, we implant several residual-like blocks using shortcut connections to augment the representational capacity of SRAE, and to simplify the training of a deeper network. The experimental evidence demonstrates that our DreamNet can achieve improved accuracy with increased depth. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： pattern recognition

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Mining High Quality patterns Using Multi-Objective Evolutionary Algorithm (Extended Abstract)

Mining High Quality Patterns Using Multi-Objective Evolution...

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International Conference on Data engineering

作者： Wei Fang Qiang Zhang Jun Sun Xiaojun Wu International Joint Laboratory on Artificial Intelligence of Jiangsu Province Jiangsu Provincial Engineering Laboratory of Pattern Recognition and Computational Intelligence Jiangnan University Wuxi China

Most studies on pattern mining have considered only one pattern, such as frequent pattern or high-utility pattern, which is difficult to meet the increasingly diverse needs of users. In this paper, a novel multi-objective problem model for high quality pattern mining (HQPM) is proposed, where the objectives are support, occupancy, and utility. In order to solve the proposed three-objective problem efficiently, an improved multi-objective evolutionary algorithm for HQPM (MOEA-PM) is proposed with two kinds of population initialization strategies and an auxiliary tool. Experimental results on real-world datasets show that the proposed three-objective problem model with the MOEA-PM algorithm can discover patterns that are both frequently occurring and has a high utility in the transaction datasets, while at the same time being relatively complete. The proposed algorithm outperforms the state-of-the-art in terms of efficiency, quality, and convergence speed.

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