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Pruning-Based Ensemble Tree for Multi-Class Classification 6

Pruning-Based Ensemble Tree for Multi-Class Classification

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6th international conference on Electrical Engineering and Information and Communication Technology, ICEEICT 2024

作者： Mitu, Maliha Mehjabin Arefin, Sadot Saurav, Zulkarnain Hasan, Md. Afanur Farid, Dewan Md. United International University Department of Computer Science and Engineering Dhaka1212 Bangladesh

ISBN: (纸本)9798350385779

Decision tree is one of the most popular classifiers in supervised learning to address classification tasks. It is easy to build and easy to understand. It is a divide-and-conquer algorithm and provides a clear path for making decisions. Decision tree can be used for both regression and classification problems. It finds the best splitting features as a root node and create sub-datasets. A leaf node of decision tree represents a decision where all and majority of the instances of a sub-dataset belongs to a particular decision or class-value. the commonly used algorithms are ID3 (Iterative Dichotomiser 3), C4.5 (improvement over ID3 algorithm), and CART (Classification And Regression Tree). Decision tree can be easily scalable and apply for Big data mining. In this paper, we have proposed a new pruning-based tree ensemble method where each tree is build from the each input feature of the dataset. therefore, $N$ number of trees will be created for $N$ number of features. then, we apply post pruning technique on each tree and select the best trees according to their cost-complexity. We have considered the size and accuracy of the tree to select in the ensemble process. the proposed approach surpasses the performance of the C4.5 classifier by almost 11 % for the selected benchmark datasets taken from the UCI machine learning repository. © 2024 IEEE.

关键词： Decision trees

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A Supervised Approach for Efficient Video Anomaly Detection Using Transfer learning 10th

A Supervised Approach for Efficient Video Anomaly Detection ...

引用

10th Biennial international conference on pattern recognition and machine Intelligence (PReMI)

作者： Kommanduri, Rangachary Ghorai, Mrinmoy Indian Inst Informat Technol Comp Vision Grp Sri City India

ISBN: (纸本)9783031451690;9783031451706

Video anomaly detection is a complex task that has numerous applications in video surveillance. It involves identifying unusual patterns or events in a video stream that deviate from the expected or typical behavior. this paper introduces a new framework for supervised video anomaly detection using transfer learning from a pre-trained model. the approach presented in this paper utilizes the MobileNetV2 architecture as a feature extractor, which is further fine-tuned using a small set of annotated data. the fine-tuned model is then utilized to classify video frames into normal or anomalous classes. the suggested methodology is evaluated on benchmark datasets and compared with state-of-the-art methods. the experimental results demonstrate the effectiveness and efficiency of the proposed method in detecting anomalies in videos with high accuracy and low computational cost.

关键词： anomaly classification transfer learning pre-trained model

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CC-HIT: Creating Counterfactuals from High-Impact Transitions

CC-HIT: Creating Counterfactuals from High-Impact Transitio...

引用

international Workshops which were held in conjunction with the 6th international conference on Process mining, ICPM 2024

作者： Xian, Zhicong Zellner, Ludwig Tavares, Gabriel Marques Seidl, thomas Database Systems and Data Mining LMU Munich Munich Germany Munich Center for Machine Learning Munich Germany

ISBN: (纸本)9783031822247

Smooth process execution relies on high-quality insights extracted from event data. For instance, trace durations heavily affect performance and increase resource consumption. While many predictive systems aim to identify these inefficiencies, they often focus on individual process instances, missing the global perspective. It is essential to detect where delays occur globally and pinpoint specific activity transitions causing them. To address this, we propose CC-HIT (Creating Counterfactuals from High-Impact Transitions), which identifies temporal activity dependencies across the process. CC-HIT uses a modified game theoretic approach and counterfactual information to generate reference event logs to estimate the consequences of activity transitions. It highlights key activity transitions impacting process performance, offering actionable insights for optimization. Validation on the BPIC 2020 dataset demonstrates its effectiveness over baseline methods. © the Author(s) 2025.

关键词： Counterfactuals Impact Ranking Local Explanation Shapley Value

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Efficient and Flexible Sublabel-Accurate Energy Minimization 26

Efficient and Flexible Sublabel-Accurate Energy Minimization

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26th international conference on pattern recognition / 8th international Workshop on Image mining - theory and Applications (IMTA)

作者： Nurlanov, Zhakshylyk Cremers, Daniel Bernard, Florian Univ Bonn Bonn Germany Tech Univ Munich Munich Germany

ISBN: (数字)9781665490627

ISBN: (纸本)9781665490627

We address the problem of minimizing a class of energy functions consisting of data and smoothness terms that commonly occur in machine learning, computer vision, and pattern recognition. While discrete optimization methods are able to give theoretical optimality guarantees, they can only handle a finite number of labels and therefore suffer from label discretization bias. Existing continuous optimization methods can find sublabel-accurate solutions, but they are not efficient for large label spaces. In this work, we propose an efficient sublabel-accurate method that utilizes the best properties of both continuous and discrete models. We separate the problem into two sequential steps: (i) global discrete optimization for selecting the label range, and (ii) efficient continuous sublabel-accurate local refinement of a convex approximation of the energy function in the chosen range. Doing so allows us to achieve a boost in time and memory efficiency while practically keeping the accuracy at the same level as continuous convex relaxation methods, and in addition, providing theoretical optimality guarantees at the level of discrete methods. Finally, we show the flexibility of the proposed approach to general pairwise smoothness terms, so that it is applicable to a wide range of regularizations. Experiments on the illustrating example of the image denoising problem demonstrate the properties of the proposed method. the code reproducing experiments is available on https://***/nurlanov-zh/sublabel-accurate-alpha-expansion.

关键词： Computer vision Codes Memory management Optimization methods machine learning Minimization Relaxation methods

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Task-Incremental Medical Image Classification with Task-Specific Batch Normalization 6th

Task-Incremental Medical Image Classification with Task-Spec...

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6th Chinese conference on pattern recognition and Computer Vision (PRCV)

作者： Xie, Xuchen Xu, Junjie Hu, Ping Zhang, Weizhuo Huang, Yujun Zheng, Weishi Wang, Ruixuan Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China Peng Cheng Lab Shenzhen Peoples R China Xinjiang Univ Sch Informat Sci & Engn Urumqi Peoples R China MOE Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9789819985579;9789819985586

Currently, multiple intelligent diagnosis systems are developed independently partly due to the difficulty in collecting training data for all tasks of disease diagnosis. It would be better if an intelligent diagnosis system can incrementally learn more diagnosis tasks as what general practitioners have done. Existing approaches to such a task-incremental learning problem still more or less suffer from the catastrophic forgetting issue, i.e., system performance on old tasks is often gradually decreased when the system incrementally learns new tasks. Here a simple but effective approach is proposed to solve the catastrophic forgetting of old task knowledge for task-incremental learning. Specifically, when training a convolutional neural network (CNN) classifier to incrementally learn more tasks, the kernels in the task-shared CNN feature extractor are initially learned or fine-tuned from the first task and then fixed through subsequent learning tasks, and only the task-specific batch normalization parameters in the CNN feature extractor and the new task-specific classifier head are learned and stored for each new task. Empirical evaluation on medical image datasets supports that the task-specific batch normalization provides an effective solution for an intelligent system to incrementally learn multiple tasks of disease diagnosis. the source code will be released publicly.

关键词： Task-incremental learning Task-specific batch normalization Intelligent diagnosis

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Research on Road Condition recognition Based on Improved YOLOv5 Algorithm 6

Research on Road Condition Recognition Based on Improved YOL...

引用

6th international conference on Information Technologies and Electrical Engineering, ICITEE 2023

作者： Jiang, Weijie School of Automotive Engineering Wuhan University of Technology Wuhan China

ISBN: (纸本)9798400708299

With the development of traffic system, real-time monitoring and accurate identification of road traffic conditions become increasingly important. this paper proposes an intelligent traffic monitoring solution based on YOLOv5 algorithm in visible light imaging system, aiming at improving the NMS algorithm of YOLOv5 algorithm by selecting appropriate activation function, improving IoU algorithm used in screening candidate boxes, and improving the recognition accuracy of the whole algorithm for complex road conditions, so as to improve the detection effect of the whole system. Finally, two data sets are selected for model learning and testing. it is verified that the recognition accuracy of the improved algorithm is improved by about 2.5 percentage points compared with the original YOLOv5 algorithm. © 2023 ACM.

关键词： machine learning

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pattern Discovery in an EEG database of Depression Patients: Preliminary Results 14

Pattern Discovery in an EEG Database of Depression Patients:...

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14th international conference on Measurement, MEASUREMENT 2023

作者： Hlavackova-Schindler, Katerina Pacher, Christina Plant, Claudia Lazarenko, Mykola Palus, Milan Hlinka, Jaroslav Kathpalia, Aditi Brunovsky, Martin University of Vienna Data Mining and Machine Learning Research Group Faculty of Computer Science Vienna Austria Czech Academy of Sciences Institute of Computer Science Department of Complex Systems Prague Czech Republic National Institute of Mental Health Clinical Research Programme Klecany Czech Republic

ISBN: (纸本)9788097262976

the ability to predict response to medication treatment of depressed patients, either early in the course of therapy or before treatment even begins can avoid trials of ineffective therapy and save patients from prolonged intervals of suffering. Symptom alleviation requires 4-6 weeks after starting current antidepressive medication. Based on the data basis of the patients and their EEG before and on the 7th day of treatment we apply data mining, causal discovery and machine learning approaches to discover interactive patterns between patient's brain regions to separate the treatment responders from non-responders. In this paper we report the preliminary results of our international project "learning Synchronization patterns in Multivariate Neural Signals for Prediction of Response to Antidepressants"ongoing at the University of Vienna, the Czech Academy of Sciences and the National Institute of Mental Health in the Czech Republic. © 2023 Institute of Measurement Science, Slovak Academy o.

关键词： data mining

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Comparative Examination of Fake News recognition Using an Ensemble of Numerous machine learning Algorithms 6

Comparative Examination of Fake News Recognition Using an En...

引用

6th international conference on Information Systems and Computer Networks, ISCON 2023

作者： Saraswat, Shipra Faujdar, Neetu Sharma, Sachin Abesit Ghaziabad Affiliated to Aktu Dept. of Computer Science and Engineering Uttar Pradesh Lucknow India GLA University Dept. of Cea Uttar Pradesh Mathura India

ISBN: (纸本)9798350346961

the global approach in getting fraud material or data over several online platforms is really a problematic nowadays. Wide dispersion of phony news adversely affects people and society all in all. In this manner, identifying counterfeit news distributed on the different online media sites has of late become an advancing research region that is drawing extraordinary consideration. Identifying the in escapable fraud data or news on various online media platforms illustrates novel difficulties regarding calculations incapable or not relevant any longer. Authors of this paper investigates diverse basic features that can be employed to distinct fraud data material from genuine. By employing those properties, authors train several AI algorithms using diverse gathering approaches and evaluate their performance on four popular datasets. As this research is based on comparative study will be done from Ensemble of several machine learning Algorithms. While the instruments are helpful, to make a more complete finish to end arrangement. the aim of this paper is to represent more troublesome situations were solid sources and creators discharge counterfeit news.. © 2023 IEEE.

关键词： Crime

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Reinforcement learning Fault Diagnosis Method Based on Less Tag data 6th

Reinforcement Learning Fault Diagnosis Method Based on Less ...

引用

conference of the-Efficiency-and-Performance-Engineering-Network (TEPEN) / 6th international conference on Maintenance Engineering (IncoME)

作者： Xin, Kuo Wang, Jianguo Zhang, Wenxing Inner Mongolia Univ Sci & Technol Coll Mech Engn Baotou 014010 Inner Mongolia Peoples R China

ISBN: (纸本)9783030990756;9783030990749

Vibration signals are often used in the fault diagnosis of rotating machinery. However, due to the influence of complex environment, environmental noise is often doped, and the diagnostic accuracy is reduced. the traditional deep self-encoder is used in the noise reduction process of rotating machinery fault diagnosis. the pooling model is poor and easy to lead to over-fitting problems, and deep learning training needs a large number of labeled data. therefore, this paper proposes a reinforcement learning fault diagnosis method based on less label data. the random pooling is used to replace the pooling layer of the original convolutional self-encoder, and the exponential linear unit (ELU) is used to replace the original activation function to enhance the convolutional self-encoder. A large number of unlabeled samples are used for training, and then the deep reinforcement learning is used for network fine tuning. the experimental results of the sensor data collected by the fault diagnosis test bench show that the method used has a good improvement in denoising ability and feature extraction ability, and the recognition accuracy and stability are better than traditional convolutional autoencoder and traditional machine learning methods.

关键词： Reinforcement learning Stochastic pooling Convolutional auto-encoder Fault diagnosis

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data Augmentation with Large Language Models for Vietnamese Abstractive Text Summarization 6

Data Augmentation with Large Language Models for Vietnamese ...

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6th international conference on Multimedia Analysis and pattern recognition, MAPR 2023

作者： Le, Huy M. Luong, Vy T. Luong, Ngoc Hoang University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Viet Nam

ISBN: (纸本)9798350327410

Text summarization plays a crucial role in managing the overwhelming volume of information available today. this task aims to condense large amounts of information into summaries. However, the lack of large-scale annotated data in certain languages, such as Vietnamese, poses a substantial challenge for developing effective summarization models. With the recent advancements in large language models, such as GPT-3.5, there is an opportunity to leverage these models to augment data for improving the performance of deep learning models in Vietnamese text summarization. In this paper, we propose an automatic approach that utilizes a large language model to generate additional training examples and to enhance the summarization process for Vietnamese texts. © 2023 IEEE.

关键词： Deep learning

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