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MetaGB: A Gradient Boosting Framework for Efficient Task Adaptive Meta learning 21

MetaGB: A Gradient Boosting Framework for Efficient Task Ada...

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Dong, Manqing Yao, Lina Wang, Xianzhi Xu, Xiwei Zhu, Liming Univ New South Wales Comp Sci & Engn Sydney NSW Australia Univ Technol Sydney Sch Comp Sci Sydney NSW Australia CSIRO Data61 Sydney NSW Australia

ISBN: (纸本)9781665423984

Deep learning frameworks generally require sufficient training data to generalize well while fail to adapt on small or few-shot datasets. Meta-learning offers an effective means of tackling few-shot scenarios and has drawn increasing attention in recent years. Meta-optimization aims to learn a shared set of parameters across tasks for meta-learning while facing challenges in determining whether an initialization condition can be generalized to tasks with diverse distributions. In this regard, we propose a meta-gradient boosting framework that can fit diverse distributions based on a base learner (which learns shared information across tasks) and a series of gradient-boosted modules (which capture task-specific information). We evaluate the model on several few-shot learning benchmarks and demonstrate the effectiveness of our model in modulating task-specific meta-learned priors and handling diverse distributions.

关键词： Meta learning Few-shot learning Optimization

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Self-Supervised Contrastive Representation learning for Time-Series Classification

Self-Supervised Contrastive Representation Learning for Time...

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international SoC Design conference, ISOCC

作者： Kunpeng Li Jong-Chul Lee Department of Electronic Convergence Engineering Kwangwoon University Seoul South Korea

ISBN: (数字)9798350377088

ISBN: (纸本)9798350377095

Time series lacks intuitive features, which increases the duration of annotation. Therefore, this paper proposes a self-supervised temporal contrastive model. By automatically extracting useful representations from unlabeled data, the model then uses a small amount of labeled data to achieve classification of time series. Extensive experiments demonstrate that the framework we propose possesses efficient and powerful learning capabilities.

关键词： Representation learning Annotations Time series analysis Feature extraction Transformers data models data mining

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Textionnaire: An NLP-Based Questionnaire Analysis Method for Complex and Ambiguous Task Decision Support 21

Textionnaire: An NLP-Based Questionnaire Analysis Method for...

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21st IEEE international conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： Rashidiani, Sajjad Doyle, Thomas E. Samavi, Reza Duncan, Laura Pires, Paulo Sassi, Roberto McMaster University Department of Electrical and Computer Engineering Hamilton Canada School of Biomedical Engineering McMaster University Hamilton Canada Vector Institute for Artificial Intelligence Toronto Canada Toronto Metropolitan University Department of Electrical Computer and Biomedical Engineering Toronto Canada McMaster University Department of Psychiatry and Behavioural Neurosciences Hamilton Canada University of British Columbia Department of Psychiatry Vancouver Canada

ISBN: (纸本)9781665490849

Questionnaires are one of the most popular means of gathering information in many domains. Traditional health surveys, particularly those focusing on specific populations of patients or certain conditions tend to have sample sizes that are too small to apply advanced artificial intelligence methods. In this study, we introduce a method that enables researchers to apply deep learning algorithms on small questionnaire datasets. We introduce a method to represent the information of fixed response questions of questionnaires in text format analogous to a word presentation for a number stored in expanded form. Then we use a pre-Trained, domain-relevant deep learning model for questionnaire classification. We applied this method to two databases: i) a hospital-maintained database of mental health questionnaires linked with hospital administrative resource uti-lization data, and ii) a similarly structured publicly available dataset, the National Survey on Drug Usage and Health. In all experiments, the deep pre-Trained model had a better Area Under Receiver Operating Characteristic Curve (AUROC) than a 3-layer artificial neural network (ANN). The proposed method enhanced classification performance by 22.95 % on Mental Health private data and 16.679% on small sub-sample of NSDUH data compared to a baseline ANN machine learning algorithm. Also, our technique improved the performance by up to 15.38% when tested for generalizability of classification. © 2022 IEEE.

关键词： Natural language processing systems

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automated and Efficient Sparsity-based Feature Selection via a Dual-component Vector 21

Automated and Efficient Sparsity-based Feature Selection via...

引用

21st IEEE international conference on data Mining (IEEE ICDM)

作者： Hoai Bach Nguyen Xue, Bing Zhang, Mengjie Victoria Univ Wellington Sch Engn & Comp Sci Wellington New Zealand

ISBN: (纸本)9781665424271

Recently, sparsity-based feature selection has gained much attention due to its good performance and potentially high interpretability. Most existing sparsity-based approaches rank original features based on their coefficients. The ranking mechanism requires a pre-defined number of selected features which is usually unknown. It also introduces a risk of selecting top-ranked but redundant features. In this paper, we address the above issues by proposing a dual-component vector in which one component represents coefficients of features, and the other component determines the decisions (selected/discarded) of features. While the former exposes relevant features with high coefficients, the latter automatically defines the number of selected features and avoids selecting redundant features. The dual-component vector is optimised by a population-based optimisation approach utilising simple vector operations (addition/subtraction) which are much more efficient than complex matrix operations (multiplication/inverse) used by existing sparsity-based approaches. Extensive experiments conducted on synthetic and real-world datasets demonstrate the superiority of the proposed algorithm in terms of effectiveness and efficiency compared with well-known and state-of-the-art feature selection approaches.

关键词： Feature Selection Dimensionality Reduction Classification Evolutionary learning

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Research and Implementation Of Image Recognition of Tea Based on Deep learning 21

Research and Implementation Of Image Recognition of Tea Base...

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21st ACIS international Winter conference on Software engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD-Winter)

作者： Gao, Mucong Shi, Minyong Li, Chunfang Commun Univ China Sch Comp Sci & Cybersecur Beijing Peoples R China

ISBN: (纸本)9781665418430

With the advent of the Internet era, deep learning technology is developing rapidly in a wide range of application scenarios. As one of the main research methods of deep learning, convolutional neural network is widely used in the field of image recognition[5]. This paper hopes to build a modern intelligent tea image recognition system by combining in-depth learning technology, so as to help inherit traditional Chinese culture. The main work of this paper is as follows: (1) Making a sample data set containing about 49000 pictures;(2) Constructing convolution neural network;(3) The correct rate of training image recognition model is 96%;(4) Realize tea picture recognition;(5) Build the web page. Under the framework of tensorflow, the program of this subject realizes the tea picture recognition function by training convolution neural network[5][6], and builds it in the beautiful web page, so that the tea culture can be presented and spread in a more modern form, which is conducive to the inheritance and development of Chinese traditional culture.

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AJAN: An engineering Framework for Semantic Web-Enabled Agents and Multi-Agent Systems 21st

AJAN: An Engineering Framework for Semantic Web-Enabled Age...

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21st international conference on Practical Applications of Agents and Multi-Agent Systems, PAAMS 2023

作者： Antakli, André Kazimov, Akbar Spieldenner, Daniel Rojas, Gloria Elena Jaramillo Zinnikus, Ingo Klusch, Matthias German Research Center for Artificial Intelligence Saarland Informatics Campus Saarbruecken Germany

ISBN: (纸本)9783031376153

The development of Semantic Web-enabled intelligent agents and multi-agent systems still remains a challenge due to the fact that there are hardly any agent engineering frameworks available for this purpose. To address this problem, we present AJAN, a modular framework for the engineering of agents that builds on Semantic Web standards and Behavior Tree technology. AJAN provides a web service-based execution and modeling environment in addition to an RDF-based modeling language for deliberative agents where SPARQL-extended behavior trees are used as a scripting language to define their behavior. In addition, AJAN supports the modeling of multi-agent coordination protocols while its architecture, in general, can be extended with other functional modules as plugins, data models, and communication layers as appropriate and not restricted to the Semantic Web. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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Deep Reinforcement learning based Dynamic Channel Bonding for Wi-Fi Networks 21

Deep Reinforcement Learning based Dynamic Channel Bonding fo...

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21st IEEE international Symposium on Parallel and Distributed Processing with Applications, 13th IEEE international conference on Big data and Cloud Computing, 16th IEEE international conference on Social Computing and Networking and 13th international conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Chen, Hao Liu, Peng You, Lizhao Guo, Ziyang Luo, Jiajun Sun, Xinghua Wang, Taotao Fu, Liqun Xiamen University Department of Information and Communication Engineering China Shenzhen Research Institute of Xiamen University Shenzhen China Huawei Technologies Co. Ltd. Wireless Technology Lab 2012 Laboratories China Sun Yat-sen University School of Electronics and Communication Engineering China Shenzhen University College of Electronics and Information Engineering Shenzhen China

ISBN: (纸本)9798350329223

This paper presents a decentralized Deep Reinforcement learning (DRL)-based dynamic channel bonding (DCB) algorithm (i.e., drlDCB) for Wi-Fi networks. Most existing RL-based channel bonding algorithms either are centralized (i.e., requiring a central controller and global information for training), or can easily fall into local optimum (e.g., over-using channels for demand) as the number of stations increases. Moreover, they may converge slowly due to the use of measured throughput or delay over a duration as reward. In contrast, drlDCB is a purely distributed algorithm where each access point gets trained using local information, and addresses the local optimum and the slow convergence problems through the following new designs: 1) a new piecewise throughput model to avoid occupying too many channels if demand is satisfied, and 2) a new reward design using estimated throughput. The use of estimated throughput allows per-frame adaptation and fast convergence speed, while the accurate estimation generalizes the application scenarios, allowing an offline-trained model to be used in a dynamic environment. Simulation results in the static scenario with fixed loads and the dynamic scenario with random loads show that drlDCB outperforms state-of-the-art DCB algorithms in terms of network throughput, delay and convergence speed. © 2023 IEEE.

关键词： Reinforcement learning

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ERODRL: Efficient ISP Routing Optimization via Deep Reinforcement learning 21

ERODRL: Efficient ISP Routing Optimization via Deep Reinforc...

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作者： Chen, Changrui Chen, Xiaolin Zeng, Deze Kunming University of Science & Technology School of Information Engineering & Automation Kunming China Qiongtai Normal University School of Information Science & Technology Haikou China China University of Geosciences School of Computer Science Wuhan China

ISBN: (纸本)9798350329223

Routing optimization is a critical issue to Internet Service Providers (ISPs) since it is highly related to the network traffic management efficiency. state-of-the-art approaches utilize Reinforcement learning (RL) technology to minimize Maximum Link Utilization (MLU). However, these approaches have two significant shortcomings: 1) the rudimentary structure of Neural Networks (NNs) restricts their ability to capture network traffic patterns accurately, resulting in sub-optimal critical flow selection, and 2) the training algorithm is ineffective in utilizing experience, leading to training instability and prolonged duration. To overcome the above shortcomings, we propose ERODRL, a network traffic management scheme which incorporates a RL based critical flow selection strategy and a flow rerouting algorithm, towards the goal of MLU minimization. In details, the RL based critical flow selection strategy incorporates a Convolutional Neural Network (CNN) block to analyze traffic features and utilizes the GELU activation function to improve traffic distribution matching. It then introduces full connection layers to the actor and critic networks respectively, allowing for detailed traffic behavior analysis. To improve training efficiency, a trust region algorithm is also involved fast the training speed of RL agent. After the critical flow are identified, the flow routing problem is formulated into Linear Programming (LP) form and a low-complexity rerouting solution is proposed to better fit the traffic patterns of ISPs. Extensive simulation results are conducted and the results demonstrate that ERODRL improves training speeds by 2.3 to 8.7 times compared to previous RL-based schemes, and reduces the MLU by 43.24% and 16.01% averagely compared to state-of-the-art solutions. © 2023 IEEE.

关键词： Reinforcement learning

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Research on intelligent Community 'Housekeeper' System Based on Artificial Intelligence 5

Research on Intelligent Community 'Housekeeper' System Based...

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5th IEEE international conference on Electronics Technology, ICET 2022

作者： Zeng, Yuting Wu, Qinglin Wuhan Business University School of Information Engineering Wuhan China

ISBN: (纸本)9781665485081

'Intelligence' is a new trend of the development of science and technology in the 21st century. With the progress of the society and the acceleration of the pace of life as well as the continuous improvement of people's requirements for themselves, the traditional community has been unable to meet the needs and dependence of modern residents. In the current situation and background of the intelligent community to carry out a 'keeper' system design and system implementation and operation. By using the advantages and technologies of big data and combining the advanced information technology means such as Internet of Things and cloud computing, it provides a good platform environment for the development of smart community, maximizes its functions, improves work efficiency, reduces the waste of manpower and material resources, and becomes an intelligent equipment developed by city and community property managers for more convenient work. © 2022 IEEE.

关键词： Systems analysis

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Meta-learning Based Feature Selection for Clustering 22nd

Meta-Learning Based Feature Selection for Clustering

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22nd international conference on intelligent data engineering and automated learning

作者： Taratukhin, Oleg Muravyove, Sergey ITMO Univ St Petersburg Russia

ISBN: (纸本)9783030916077;9783030916084

Clustering is a highly demanded task nowadays. However, it requires the attention of human experts and it might certainly benefit from automation. This paper presents a method to perform simultaneous automatic algorithm selection and hyperparameter tuning with feature selection for clustering. The algorithm also features a meta-model to predict promising algorithm configurations based on dataset properties. Experimental results are provided for a set of benchmark datasets, it is shown that the proposed method outperforms known alternatives.

关键词： Machine learning Clustering Feature selection Meta-learning

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