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检索条件"任意字段=5th International Conference on Machine Learning and Data Mining in Pattern Recognition"

共 3165 条记录，以下是531-540 订阅

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A General Framework for Finding the Optimal Imbalance Ratio in Sampling Methods 5

A General Framework for Finding the Optimal Imbalance Ratio ...

引用

5th IEEE international conference on Electronics Technology, ICET 2022

作者： Peng, Jialin Shao, Yabin Huang, Longhai Chongqing University of Posts and Telecommunications College of Chongqing Key Laboratory of Computational Intelligence Chongqing China

ISBN: (纸本)9781665485081

How to obtain better classification results from imbalance data has always been a research hot spot in the neighborhood of machine learning and data mining. At present, there are many techniques such as sampling and cost-sensitive learning algorithms to reduce the negative impact of imbalance on classification performance. Some scholars start with the relationship between imbalance ratio and classification performance, hoping to improve classification performance. In this paper, the classification performance is mainly improved by improving the sampling method. Considering that many invalid samples may be synthesized, this paper defines a metric of distribution difference in the sampling process. then, by analyzing the relationship between the distribution difference and the classification performance, the optimal imbalance ratio in the sampling process can be found. Based on some classic general sampling methods, experimental results on some real data sets prove the effectiveness of the framework. © 2022 IEEE.

关键词： learning algorithms

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Uncertainty in Ambiguity of data 27th

Uncertainty in Ambiguity of Data

引用

27th international conference on pattern recognition, ICPR 2024

作者： Trinh, Hoang-Hiep Hanoi University of Science and Technology Hanoi Viet Nam

ISBN: (纸本)9783031781063

the current deep learning models have to deal with overconfidence issues due to overfitting and over-parameterization. therefore, uncertainty estimation is an essential step in understanding and explaining the model’s predictions to provide warnings and help humans trust the predictions of the AI model, especially in precise high-risk tasks, low regime data, and ambiguous datasets. this paper introduces an ambiguous dataset namely the YSH dataset and proposes a novel architecture, named the Diversity Ensemble of ResNet (DEResNet), to address the challenging uncertainty and diversity in the YSH dataset. Specifically, we use the Stein Variational Gradient Descent (SVGD) algorithm for uncertainty estimation. then we utilize a diversity mutual information (MI) loss on target data to attack the diversity problem. DEResNet can address comprehensive uncertainty both in parameter space and output space. Extensive experiments demonstrate that our model outperforms the baseline method in the YSH dataset. Furthermore, our proposal also has the potential to learn the set of solutions for an ambiguous task and help find different solutions for optimization problems. Our code is available here https://***/trinh-hoang-hiep/DEResNet. © the Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Power Consumption Behavior Analysis Method Based on Improved Clustering Algorithm of Big data Technology 5th

Power Consumption Behavior Analysis Method Based on Improved...

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5th EAI international conference on Multimedia Technology and Enhanced learning, ICMTEL 2023

作者： Zhu, Zheng Chen, Haibin Xiao, Shuang Yan, Jingrui Wu, Lei Electric Power Research Institute State Grid Shanghai Municipal Electric Power Company Shanghai200120 China LongShine Technology Group Co. Ltd. Shanghai201600 China

ISBN: (纸本)9783031505768

With the increase of the types and quantities of electrical equipment, the magnitude of user power consumption data has increased exponentially. Deep mining and analysis of it is the key to help the power grid understand customer needs. therefore, the research on user power consumption analysis method based on improved clustering algorithm based on big data technology is proposed. Analyze the influencing factors of users’ electricity use behavior (economic factors, time factors, climate factors and other factors), on this basis, explore the characteristics of users’ electricity load, reduce and process users’ electricity data samples based on PCA algorithm, improve clustering algorithm by using big data technology – extreme learning machine principle, cluster and process users’ electricity use behavior, use GSP algorithm to mine sequential patterns of users’ electricity use behavior, and obtain users’ electricity use rule model, thus, the analysis of users’ electricity consumption behavior is realized. the experimental data show that the minimum value of MIA index obtained by applying the proposed method is 0.09, and the maximum accuracy of the user power consumption law model is 92%, which fully confirms that the proposed method has better application performance. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Electric power utilization

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Research on Differential Privacy Optimization Algorithm Based on DPSGD Model 5

Research on Differential Privacy Optimization Algorithm Base...

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2023 5th international conference on Artificial Intelligence and Computer Applications, ICAICA 2023

作者： Wang, Jiaqi Mao, Dong Zhang, Jiansong Rao, Hanyu Wang, Yidan Zheng, Yingqin State Grid Zhejiang Electric Power Company Information & Telecommunication Branch Zhejiang Hangzhou310016 China State Grid Zhejiang Electric Power Co. Ltd. Zhejiang Hangzhou310063 China Shanghai Fushu Technology Co. Ltd. Shanghai200136 China

ISBN: (纸本)9798350323313

this project studies a differential privacy stochastic gradient descent algorithm (DPSGD) based on MapReduce. the research direction of this article is to carry out research on efficient privacy protection issues for distributed data processing. Add Gaussian white noise pattern cropping to existing slope correction methods, and make some modifications to the gradient correction algorithm. A user information protection strategy based on user information has been proposed, and improvements have been made to the user information protection algorithm using this strategy. these improvements can map the privacy damage accumulation function to the sphere of the Frobenius module during the iteration process, which can improve its convergence performance. © 2023 IEEE.

关键词： machine learning

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Cost-Effective Peak Shaving Strategy Based on Clustering and XGBoost Algorithm 5

Cost-Effective Peak Shaving Strategy Based on Clustering and...

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5th international conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Lim, Sol Gantassi, Rahma Choi, Yonghoon Chonnam Natl Univ Dept Elect Engn Gwangju 61186 South Korea

ISBN: (纸本)9781665456456

In a cost-effective peak shaving strategy, clustering and machine learning algorithm can be used to set optimal peak shaving time zone for each load. Energy Storage System (ESS) charge amount is determined with load prediction data through machine learning model, and the peak shaving time zone is adjusted flexibly according to load patterns for each cluster. It is possible to prevent ESS from being overcharged or undercharged through load prediction. In addition, rather than applying peak shaving collectively at the on-peak time, efficient operation of the power grid can be expected by adjusting the time zone flexibly for each power usage pattern. the effectiveness of the proposed system model is to be proved through changes in electricity cost depending on whether it is introduced or not.

关键词： clustering cost minimization K-means machine learning peak shaving XGBoost

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Research on data Fusion Algorithms for Non-stop Overload Detection on Highways

Research on Data Fusion Algorithms for Non-stop Overload Det...

引用

pattern recognition and machine learning (PRML), IEEE international conference on

作者： Lian Tang Qing Liu Hang Lin School of Information Engineering Chongqing Jianzhu College Chongqing

ISBN: (数字)9798350355925

ISBN: (纸本)9798350355932

With the rapid development of highway network, overloading of freight vehicles has caused serious impact on road safety and infrastructure. In order to improve the data fusion accuracy of the overload control system, combining dynamic weighing, image capture recognition and laser scanning technologies, a complete set of non-stop detection system architecture is researched and designed, a multi-feature volume data fusion model is constructed, and the data matching and fusion process is optimized. through the application test of comprehensive super-control platform, the results show that the proposed data fusion algorithm of non-stop detection for highway super-control can effectively control the error of total weight within ± 2.5%, and the errors of vehicle length and height within ± 150mm and ± 50mm, respectively, which verifies its application value in highway super-control.

关键词： Road transportation Weight measurement Technological innovation Accuracy Heuristic algorithms Transportation industry data integration Transportation Systems architecture Vehicle dynamics

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Bit-level Malicious Traffic Detection Based on Markov Images and Deep learning 5

Bit-level Malicious Traffic Detection Based on Markov Images...

引用

5th international conference on pattern recognition and Artificial Intelligence, PRAI 2022

作者： Cao, Xin Luo, Qin Liu, JinZhou Southwest Petroleum University School of Computer Science Chengdu China Chengdu Polythchnic Software College Chengdu China

ISBN: (数字)9781665499163

ISBN: (纸本)9781665499163

In recent years, with the rapid development of network services, network malicious attacks have become a serious security threat, causing huge economic losses. How to quickly and accurately detect and identify malicious network traffic is critical to network security. Since traditional machine learning-based methods are limited by feature engineering, malicious traffic grayscale maps and deep learning-based methods become effective solutions. However, converting the original traffic into grayscale images leads to information loss, which affects the detection effect. To solve this problem, this paper provides a malicious traffic i dentification me thod ba sed on Ma rkov feature images and deep learning, called MKCNN. this method regards the application layer data of the network session as a Markov chain, extracts the Markov probability transition matrix and converts it into a Markov image, and then uses a convolutional neural network to identify the Markov image. Experiments are performed on the publicly available USTC-TFC2016 dataset. the experimental results show that the method can detect malicious traffic w ith a n a ccuracy o f 9 9.99%, a nd c ompared w ith the existing methods, the method based on Markov feature images proposed in this paper has obvious advantages in detection accuracy. © 2022 IEEE.

关键词： Network security

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2D Respiratory Sound Analysis to Detect Lung Abnormalities 5th

2D Respiratory Sound Analysis to Detect Lung Abnormalities

引用

5th international conference on Recent Trends in Image Processing and pattern recognition (RTIP2R)

作者： Alice, Rafia Sharmin Wendling, Laurent Santosh, K. C. Univ South Dakota Comp Sci 2AI Appl Artificial Intelligence Res Lab Vermillion SD 57069 USA Univ Paris SIP Syst Intelligents Percept 45 Rue St Peres F-75270 Paris 06 France

ISBN: (纸本)9783031235986;9783031235993

In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. the primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients (MFCC), spectral centroid, and spectral roll-off. In our experiments, using the publicly available respiratory sound database named ICBHI 2017 (5.5 h of recordings containing 6898 respiratory cycles from 126 subjects), we received the highest performance with the area under the curve of 0.79 from Spectrogram as opposed to 0.48 AUC from the raw data from a pre-trained deep learning model: VGG16. Our study proved that 2D data representation could help better understand/analyze lung abnormalities as compared to 1D data. In addition, our results can be compared with previous works.

关键词： Lung abnormality Respiratory sound 2D data Representation Deep visual features

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Modern theoretical Tools for Understanding and Designing Next-generation Information Retrieval System 22

Modern Theoretical Tools for Understanding and Designing Nex...

引用

15th ACM international conference on Web Search and data mining (WSDM)

作者： Xu, Da Ruan, Chuanwei Walmart Labs Sunnyvale CA 94086 USA Instacart San Francisco CA USA

ISBN: (纸本)9781450391320

In the relatively short history of machine learning, the subtle balance between engineering and theoretical progress has been proved critical at various stages. the most recent wave of AI has brought to the IR community powerful techniques, particularly for pattern recognition. While many benefits from the burst of ideas as numerous tasks become algorithmically feasible, the balance is tilting toward the application side. the existing theoretical tools in IR can no longer explain, guide, and justify the newly-established methodologies. With no choices, we have to bet our design on black-box mechanisms that we only empirically understand. the consequences can be suffering: in stark contrast to how the IR industry has envisioned modern AI making life easier, many are experiencing increased confusion and costs in data manipulation, model selection, monitoring, censoring, and decision making. this reality is not surprising: without handy theoretical tools, we often lack principled knowledge of the pattern recognition model's expressivity, optimization property, generalization guarantee, and our decision-making process has to rely on over-simplified assumptions and human judgments from time to time. Facing all the challenges, we started researching advanced theoretical tools emerging from various domains that can potentially resolve modern IR problems. We encountered many impactful ideas and made several independent publications emphasizing different pieces. Time is now to bring the community a systematic tutorial on how we successfully adapt those tools and make significant progress in understanding, designing, and eventually productionize impactful IR systems. We emphasize systematicity because IR is a comprehensive discipline that touches upon particular aspects of learning, causal inference analysis, interactive (online) decision-making, etc. It thus requires systematic calibrations to render the actual usefulness of the imported theoretical tools to serve IR problem

关键词： Information retrieval theory machine learning pattern recognition Decision making Causal inference Bandit Reinforcement learning

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SPCPFS: a pseudo-label filtering strategy with fusion of perplexity and confidence 5

SPCPFS: a pseudo-label filtering strategy with fusion of per...

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5th international conference on machine learning and Natural Language Processing (MLNLP)

作者： Liu, Zhiqiang Ma, Zhiqiang Wang, Jiatai Li, Jingyi Sun, Jiaqi Bao, Caijilahu Inner Mongolia Univ Technol Coll Data Sci & Applicat Hohhot Peoples R China

ISBN: (纸本)9781450399067

In the pseudo-label filtering for semi-supervised Mongolian speech recognition, the correctness of word combinations and the correctness of speech and word correspondences in the self-training set cannot be guaranteed simultaneously. To solve this problem, we propose a pseudo-label filtering strategy with fusion of perplexity and confidence, which is called sentence perplexity confidence. the strategy simultaneously evaluates the semantic relations of pseudo-labels and the correspondence between pseudo-labels and acoustic features of unlabeled speech, which improves the accuracy of the self-training set and thus the performance of the target speech recognition model output by semi-supervised training. We conducted ablation experiments and comparison experiments of sentence perplexity confidence on Mongolian datasets IMUT-MC and IMUT-MC-SMI. the experimental results show that the sentence perplexity confidence is ahead of the sentence-level confidence and perplexity in terms of accuracy improvement ability of the self-training set, and the output target speech recognition models reach 14.7% and 16.1% for WER and SER respectively.

关键词： Semi-supervised training Pseudo-label filtering Confidence Perplexity Mongolian speech recognition

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