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检索条件"任意字段=4th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2005"

共 135 条记录，以下是1-10 订阅

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machine learning Techniques for pattern recognition in High-Dimensional data mining 4

Machine Learning Techniques for Pattern Recognition in High-...

引用

4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Li, Pochun Northeastern University Boston United States

ISBN: (纸本)9798331534622

this paper proposes a frequent pattern data mining algorithm based on support vector machine (SVM), aiming to solve the performance bottleneck of traditional frequent pattern mining algorithms in high-dimensional and sparse data environments. By converting the frequent pattern mining task into a classification problem, the SVM model is introduced to improve the accuracy and robustness of pattern extraction. In terms of method design, the kernel function is used to map the data to a high-dimensional feature space, so as to construct the optimal classification hyperplane, realize the nonlinear separation of patterns and the accurate mining of frequent items. In the experiment, two public datasets, Retail and Mushroom, were selected to compare and analyze the proposed algorithm with traditional FP-Growth, FP-Tree, decision tree and random forest models. the experimental results show that the algorithm in this paper is significantly better than the traditional model in terms of three key indicators: support, confidence and lift, showing strong pattern recognition ability and rule extraction effect. the study shows that the SVM model has excellent performance advantages in an environment with high data sparsity and a large number of transactions, and can effectively cope with complex pattern mining tasks. At the same time, this paper also points out the potential direction of future research, including the introduction of deep learning and ensemble learning frameworks to further improve the scalability and adaptability of the algorithm. this research not only provides a new idea for frequent pattern mining, but also provides important technical support for solving pattern discovery and association rule mining problems in practical applications. © 2024 IEEE.

关键词： Adversarial machine learning

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Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data 4

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

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4th international conference on Electronic Information Engineering and Computer Communication, EIECC 2024

作者： Shen, Aoran Dai, Minghao Hu, Jiacheng Liang, Yingbin Wang, Shiru Du, Junliang University of Michigan Ann Arbor United States Columbia University New York United States Tulane University New Orleans United States Northeastern University Seattle United States Dartmouth College Hanover United States Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798331534622

In the 21st-century information age, with the development of big data technology, effectively extracting valuable information from massive data has become a key issue. Traditional data mining methods are inadequate when faced with large-scale, high-dimensional and complex data. Especially when labeled data is scarce, their performance is greatly limited. this study optimizes data mining algorithms by introducing semi-supervised learning methods, aiming to improve the algorithm's ability to utilize unlabeled data, thereby achieving more accurate data analysis and pattern recognition under limited labeled data conditions. Specifically, we adopt a self-training method and combine it with a convolutional neural network (CNN) for image feature extraction and classification, and continuously improve the model prediction performance through an iterative process. the experimental results demonstrate that the proposed method significantly outperforms traditional machine learning techniques such as Support Vector machine (SVM), XGBoost, and Multi-Layer Perceptron (MLP) on the CIFAR-10 image classification dataset. Notable improvements were observed in key performance metrics, including accuracy, recall, and F1 score. Furthermore, the robustness and noise-resistance capabilities of the semi-supervised CNN model were validated through experiments under varying noise levels, confirming its practical applicability in real-world scenarios. © 2024 IEEE.

关键词： Self-supervised learning

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Text Classification Experiments on Contextual Graphs Built by N-Gram Series 4th

Text Classification Experiments on Contextual Graphs Built b...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Sen, Tarik Uveys Yakit, Mehmet Can Gumus, Mehmet Semih Abar, Orhan Bakal, Gokhan Abdullah Gul Univ TR-38080 Kayseri Turkiye Osmaniye Korkut Ata Univ TR-80000 Osmaniye Turkiye

ISBN: (纸本)9783031821493;9783031821509

Traditional n-gram textual features, commonly employed in conventional machine learning models, offer lower performance rates on high-volume datasets compared to modern deep learning algorithms, which have been intensively studied for the past decade. the main reason for this performance disparity is that deep learning approaches handle textual data through the word vector space representation by catching the contextually hidden information in a better way. Nonetheless, the potential of the n-gram feature set to reflect the context is open to further investigation. In this sense, creating graphs using discriminative ngram series with high classification power has never been fully exploited by researchers. Hence, the main goal of this study is to contribute to the classification power by including the long-range neighborhood relationships for each word in the word embedding representations. To achieve this goal, we transformed the textual data by employing n-gram series into a graph structure and then trained a graph convolution network model. Consequently, we obtained contextually enriched word embeddings and observed F1-score performance improvements from 0.78 to 0.80 when we integrated those convolution-based word embeddings into an LSTM model. this research contributes to improving classification capabilities by leveraging graph structures derived from discriminative n-gram series.

关键词： text-graph transformation graph convolution network deep learning text mining

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IProceedings of the 4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

IProceedings of the 4th International Conference on Artifici...

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4th international conference on Artificial Intelligence and Computer Engineering, ICAICE 2023

ISBN: (纸本)9798400708831

the proceedings contain 200 papers. the topics discussed include: heat-aware graph data placement strategy for NVM;parameters estimation of photovoltaic models via an improved differential evolution algorithm;study on the extraction of law enforcement relationships in administrative law enforcement instrument data;online algorithm for exploring a grid polygon with two robots;research on gesture recognition method by improving dung beetle algorithm to optimize BP neural network;an improved algorithm for frequent sequence pattern mining based on PrefixSpan-ComplexPrefixSpan;machine learning-based research on reserve prediction of natural-gas-hydrates;enhancing coal mine safety monitoring algorithm using graph computing techniques;reverse distillation support vector data description for unsupervised anomaly detection;and few-shot object counting model based on self-support matching and attention mechanism.

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Novel Hybrid Feature Selection Method and Globalization Technique for Text Classification 4th

Novel Hybrid Feature Selection Method and Globalization Tech...

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4th international conference on Intelligent Systems and pattern recognition-ISPR

作者： Mekhzoumi, Dalila Boulkaboul, Sahar Amroun, Kamal CERIST Res Ctr Algiers Ben Aknoun Algeria Abderrahmane Mira Univ Bejaia Algeria

ISBN: (纸本)9783031821523;9783031821530

Feature selection in text classification refers to the critical process of identifying and selecting the most relevant and informative features such as words, phrases, or other linguistic elements from a text dataset. this process, which has been a research topic for decades and finds applications across various fields such as bioinformatics, image recognition, image retrieval, text mining, etc., is essential for optimizing classification accuracy and efficiency. Addressing the challenge of high dimensionality in text data, stemming from the abundance of features like words or n-grams, is crucial to mitigate computational inefficiency and overfitting. Furthermore, the presence of irrelevant or redundant features in text datasets poses another significant challenge, as these features can introduce noise or irrelevant information, thereby undermining the performance of classifier. In this paper, we proposed a new approach GL-SMCHI using improved features selection method to reduce the CHI value of high-frequency words, and globalization technique to incorporate both feature and class information when evaluating the importance of each feature. We compared the results obtained from our proposal against those of existing robust alternatives, the simulation results show that the proposed method outperforms the standard CHI squared, the improved feature selection methods, and the globalization method in term of F-Score and accuracy.

关键词： Features selection CHI-Squared machine learning Globalization Text classification Support Vector machines

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ISM 2022 - 4th international conference on Industry 4.0 and Smart Manufacturing

ISM 2022 - 4th International Conference on Industry 4.0 and ...

引用

4th international conference on Industry 4.0 and Smart Manufacturing, ISM 2022

the proceedings contain 199 papers. the topics discussed include: infrastructure network support and leapfrogging Africa to Industry 4.0: the case of Tanzania;comparison of energy-use efficiency for lettuce plantation under nutrient film technique and deep-water culture hydroponic systems;an edge-cloud based reference architecture to support cognitive solutions in process industry;logistics 4.0 in intermodal freight transport;analysis of sustainable concrete obtained from the by-products of an industrial process and recycled aggregates from construction and demolition waste;intelligent concrete surface cracks detection using computer vision, pattern recognition, and artificial neural networks;spatial change recognition model using artificial intelligence to remote sensing;smart trip prediction model for metro traffic control using data mining techniques;encryption and generation of images for privacy-preserving machine learning in smart manufacturing;and using analytical and data-driven methods to develop a soft-sensor for flow rate monitoring in tube extrusion.

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machine learning Techniques for pattern recognition in High-Dimensional data mining

Machine Learning Techniques for Pattern Recognition in High-...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Pochun Li Northeastern University Boston USA

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

关键词： Support vector machines Deep learning machine learning algorithms Accuracy Scalability Robustness Classification algorithms Association rule learning data mining Random forests

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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification 4

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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4th international conference on Advances in Computing, Communication Control and Networking, ICAC3N 2022

作者： Ananthi, K. Balakrishnan, M. Pandi, M. Senthil Madasamy, N. Dr.Mahalingam College of Engineering and Technology Artificial Intelligence &data Science Tamil Nadu Coimbatore642003 India Dr.Mahalingam College of Engineering and Technology Computer Science and Engineering Tamil Nadu Coimbatore642003 India

ISBN: (纸本)9781665474368

In data mining and pattern recognition, the Support Vector machine is an extensively used classification technique. the tuning weight methods and the distance of the metrics used in a Support Vector based classification system are important. the problem of determining the best feature weight values and measuring the distance between neighbors, which affects the Support vector machine's classification accuracy, is a key obstacle. For parameter optimization, a hybrid approach is applied in this paper. To quantify the equalities between thetraining data set and the test data set are observed and the similarity measuring approach known as the fuzzy distance metric is used. Additional work, such as creating a unique fuzzy membership function and membership degrees for each dataset, adds time to the design process for these hybrid systems. To improve the classification model's precision, this automated approach will heuristically explore the optimal bounds of fuzzy membership functions for each dataset. Additionally, looking into the optimal boundaries may decrease error rates and improve accuracy in the proposed system. the experimental results will be implemented into a real-world classification challenge sourced from the UCI machine-learning benchmark library. © 2022 IEEE.

关键词： Support vector machines

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Leveraging Semi-Supervised learning to Enhance data mining for Image Classification under Limited Labeled data

Leveraging Semi-Supervised Learning to Enhance Data Mining f...

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Electronic Information Engineering and Computer Communication (EIECC), international conference on

作者： Aoran Shen Minghao Dai Jiacheng Hu Yingbin Liang Shiru Wang Junliang Du University of Michigan Ann Arbor USA Columbia University New York USA Tulane University New Orleans USA Northeastern University Seattle USA Dartmouth College Hanover USA Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798331534622

ISBN: (纸本)9798331534639

关键词： Support vector machines Visualization Accuracy Semisupervised learning Feature extraction Classification algorithms Behavioral sciences data mining Convolutional neural networks Image classification

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Bug recognition using Hybrid Fuzzy Logic algorithm and Support Vector machine classification

Bug Recognition using Hybrid Fuzzy Logic algorithm and Suppo...

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Advances in Computing, Communication Control and Networking (ICAC3N), international conference on

作者： K. Ananthi M. Balakrishnan M. Pandi N. Senthil Madasamy Artificial Intelligence &Data Science Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India Computer Science and Engineering Dr.Mahalingam College of Engineering and Technology Coimbatore Tamil Nadu India

关键词： Measurement Fuzzy logic Weight measurement Training Computer bugs Support vector machine classification machine learning

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