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A viewpoint-guided prototype network for 3D shape classification

MULTIMEDIA SYSTEMS 2023年第6期29卷 3531-3547页

作者： Han, Li He, Jinhai Dou, Feng Ma, Huiwen Xie, Xinyang Yang, Wanwen Liaoning Normal Univ Sch Comp Sci & Artificial Intelligence Dalian Peoples R China

Multi-view learning methods have achieved remarkable results in 3D shape recognition. However, most of them focus on the visual feature extraction and feature aggregation, while viewpoints (spatial positions of virtual cameras) for generating multiple views are often ignored. In this paper, we deeply explore the correlation between viewpoints and shape descriptor, and propose a novel viewpoint-guided prototype learning network (VGP-Net). We introduce a prototype representation for each class, including viewpoint prototype and feature prototype. The viewpoint prototype is the average weight of each viewpoint learned from a small support set via Score Unit, and stored in a weight dictionary. Our VGP model self-adaptively learns the view-wise weights by dynamically assembling with the viewpoint prototypes in weight dictionary and performing element-wise operation via view pooling layer. Under the guidance of viewpoint prototype, important visual features are enhanced, while those negligible features are eliminated. These refined features are effectively fused to generate compact shape descriptor. All the shape descriptors are clustered in feature embedding space, and the cluster center represents the feature prototype of each class. The classification thus can be performed by searching the nearest distance to feature prototypes. To boost the learning process, we further present a multi-stream regularization mechanism in both feature space and viewpoint space. Extensive experiments demonstrate that our VGP-Net is efficient, and the learned deep features have stronger discrimination ability. Therefore, it can achieve better performance compared to state-of-the-art methods.

关键词： classification algorithm Data analysis 3D shapes Deep learning

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Minimising Added classification Error Using Walsh Coefficients

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IEEE TRANSACTIONS ON NEURAL NETWORKS 2011年第8期22卷 1334-1339页

作者： Windeatt, Terry Zor, Cemre Univ Surrey Fac Elect & Phys Sci Ctr Vis Speech & Signal Proc Guildford GU2 7XH Surrey England

Two-class supervised learning in the context of a classifier ensemble may be formulated as learning an incompletely specified Boolean function, and the associated Walsh coefficients can be estimated without the knowledge of the unspecified patterns. Using an extended version of the Tumer-Ghosh model, the relationship between added classification error and second-order Walsh coefficients is established. In this brief, the ensemble is composed of multilayer perceptron base classifiers, with the number of hidden nodes and epochs systematically varied. Experiments demonstrate that the mean second-order coefficients peak at the same number of training epochs as ensemble test error reaches a minimum.

关键词： classification algorithm multilayer perceptrons pattern analysis pattern recognition

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A Comparison of classification algorithms Based on The Number of Features

A Comparison of Classification Algorithms Based on The Numbe...

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第三十九届中国控制会议

作者： Peichen Xiong Wei Ping Computer Science and Software Engineering Xi'an Jiaotong-Liverpool University School of Information Engineering College of Science and Technology Ningbo University

As the most widely known data mining algorithm, classification algorithms have attracted wide attention. K-Nearest Neighbor(KNN) algorithm and decision tree algorithm are the two widely known algorithms in classification algorithms. Sometimes, people not sure how to choose the suitable to solve the classification problems. In this paper, we establish KNN algorithm model and decision tree ID3 algorithm model to analyze the accuracy of the two algorithms in the same data set with different number of features. Through the learning curve and cross validation, we find ID3 algorithm is better than KNN algorithm, and when the number of feature increased the accuracy of KNN is increasing while ID3 is decreased.

关键词： KNN Decision Tree ID3 classification algorithm

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Improvement and Implementation of Feature Weighting algorithm TF-IDF in Text classification

Improvement and Implementation of Feature Weighting Algorith...

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2018 International Conference on Network, Communication, Computer Engineering (NCCE 2018)

作者： Weisi Dai Department of Informatics Beijing University of Technology

ISBN: (纸本)9781510871076

Based on the introduction of the traditional feature weighting algorithm TF-IDF,based on the phenomenon that the eigenvalue extraction is not effective when the text to be classified is not uniform,an improved TF-IDF algorithm is proposed in this paper,which considers the uneven text distribution *** experimental results show that the results obtained by the classification algorithm using the improved algorithm are better than the original algorithm in terms of accuracy and recall and make up for the defects of the original TF-IDF algorithm.

关键词： classification algorithm TF-IDF

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Hybridizing graph-based Gaussian mixture model with machine learning for classification of fraudulent transactions

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COMPUTATIONAL INTELLIGENCE 2022年第6期38卷 2134-2160页

作者： Prusti, Debachudamani Behera, Ranjan Kumar Rath, Santanu Kumar Natl Inst Technol Rourkela Dept Comp Sci & Engn Rourkela India Birla Inst Technol Dept Comp Sci & Engn Ranchi India Birla Inst Technol Dept Comp Sci & Engn Ranchi Jharkhand India

It has been observed that a good number of financial organizations often face a number of threats due to credit card fraud that affects consistently to the card holder as well as the organizations. This is one of the fastest-growing frauds of its kind and the most emerging problems for the institutions to prevent. A number of researchers and analysts have shown interest to work on this area in order to identify such issues in an effective manner by applying various supervised as well as unsupervised learning approaches. In this assessment, three classification techniques such as support vector machine (SVM), k-nearest neighbor (k-NN), and extreme learning machine (ELM) that come under supervised learning category are applied to the BankSim data to categorize the normal and fraudulent class transactions in credit card. These algorithms are incorporated with the graph features extracted from the dataset by using a database tool Neo4j. The nodes of the graph represent the transactional data samples and the edges create relationships among the nodes to find the patterns of data using connected data analysis. k-fold cross validation approach in Gaussian mixture model (GMM) has been applied for classification of the credit card transaction data in a single distribution. Further, a combined graph-based Gaussian mixture model (CGB-GMM) has been proposed to effectively detect the fraudulent instances in credit card transactions with the application of graph algorithms such as degree centrality, LPA, page rank, and so forth. Each of the learning algorithms are implemented with and without the application of graph algorithms and their performances are assessed empirically for analysis.

关键词： classification algorithm credit card fraud Gaussian mixture model graph feature Neo4j$$ Neo4j $$ database

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AI Empowered Virtual Reality Integrated Systems for Sleep Stage classification and Quality Enhancement

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IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING 2022年 30卷 1494-1503页

作者： Huang, Jing Ren, Lifeng Feng, Lifang Yang, Fan Yang, Lingfan Yan, Ke Zhejiang Gongshang Univ Sch Informat & Elect Engn Hangzhou 310018 Peoples R China Zhejiang Gongshang Univ Coll Food Sci & Biotechnol Hangzhou 310018 Peoples R China China Jiliang Univ Coll Informat Engn Hangzhou 310018 Peoples R China Zhejiang Prov Peoples Hosp Hangzhou 310003 Zhejiang Peoples R China Natl Univ Singapore Dept Built Environm Singapore 117566 Singapore

Insomnia is a common public health problem and an open biomedical research topic. Insomnia results in various health problems, including memory decline, decreases concentration and weakens problem-solving ability. The insufficient sleep also leads to skin ageing, heart disease, high blood pressure, arrhythmia and stroke. While it remains as a global health concern, sleep quality improvement using modern technologies, such as machine learning, classification technologies, virtual reality (VR), becomes an open and hot research problem. These modern technologies offer new curing solutions under certain conditions. In this paper, we present a sleeping-aid system with a single-channel electroencephalogram (EEG) sleep stage classification algorithm to improve the sleep quality. The sleeping-aid system promotes machine learning integrated VR and multimedia technology for sleep improvement. Ninety participants were invited to test on three different systems with 3D VR, 2D video, and music only. An adequate stimulus of audio-vision can be a complement of the drug treatment. The experimental results showed that the proposed method demonstrated superior performance over existing methods.

关键词： Sleep Electroencephalography Brain modeling Machine learning Visualization Drugs Servers Sleep promotion hidden Markov model classification algorithm virtual reality EEG sleep quality

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Multi-View classification via a Fast and Effective Multi-View Nearest-Subspace Classifier

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IEEE ACCESS 2019年 7卷 49669-49679页

作者： Shu, Ting Zhang, Bob Tang, Yuan Yan Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China

Multi-view data represented in multiple views contains more complementary information than a single view, whereby multi-view learning explores and utilizes the multi-view data. In general, most existing multi-view learning methods consider the correlation between multiple views. However, the relationship between classes and views which is also important in multi-view learning has never been involved in the existing works. In this paper, we propose a fast and effective multi-view nearest-subspace classifier (MV-NSC) by taking advantage of both the two relationships simultaneously. MV-NSC consists of four main parts: 1) projection residual, 2) view-dependent class separability, 3) view similarity, and 4) final decision. The last part combines the first three parts in one final decision matrix, while the first three parts utilize the information of the multi-view data in various aspects. Our proposed method is evaluated on four benchmark datasets and compared with seven other classifiers including both multi-and single-view algorithms. According to the experimental results, it shows that our proposed method is effective, efficient, and robust in multi-view classification.

关键词： Adaption model classification algorithm multi-view data nearest-subspace classifier view-dependent class separability view similarity

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Design and analysis of a general vector space model for data classification in Internet of Things

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2019年第1期2019卷 1页

作者： Sang, Jinguo Pang, Shanchen Zha, Yang Yang, Fan Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Shandong Peoples R China China Univ Petr Coll Comp & Commun Engn Qingdao 266580 Shandong Peoples R China Beijing Baidu Netcom Sci & Technol Co Ltd Beijing 100000 Peoples R China Zhongnan Univ Econ & Law Sch Informat & Secur Engn Wuhan 430073 Hubei Peoples R China

The amount of information increases explosively in Internet of Things, because more and more data are sensed by large amount of sensors. The explosive growth of information makes it difficult to access information efficiently, so it is an effective method to decrease the amount of information to be transferred on network by text classification. This paper proposes a new text classification algorithm based on vector space model. This algorithm improves the feature selection and weighting methods by introducing synonym replacement to traditional text classification algorithms. The experimental results show that the proposed classification algorithm has considerably improved the precision and recall of classification.

关键词： Internet of Things Edge computing Text classification Feature selection Feature weighting classification algorithm Vector space model

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False alarm classification for multivariate manufacturing processes of thin film transistor-liquid crystal displays

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JOURNAL OF PROCESS CONTROL 2015年 35卷 21-29页

作者： Kang, Ji Hoon Kim, Seoung Bum Samsung SDS Algorithm Res Ctr Ind Technol Lab Seoul 138240 South Korea Korea Univ Sch Ind Management Engn Seoul 136713 South Korea

Control charts have been widely used to improve manufacturing processes by reducing variations and defects. In particular, multivariate control charts have been effectively applied with monitoring processes that contain many correlated variables. Most existing multivariate control charts are vulnerable to mis-classification errors that originate because of the hypothesis tests. In particular, these often cause the generation of a large number of false alarms. In this paper, we propose a procedure to reduce false alarms by combining a multivariate control chart and data mining algorithms. Simulation and real case studies demonstrate that the proposed method effectively reduces the false alarm rate. (C) 2015 Elsevier Ltd. All rights reserved.

关键词： Multivariate control charts False alarms classification algorithm TFT-LCD manufacturing process

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Stepwise classification of cancer samples using clinical and molecular data

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BMC BIOINFORMATICS 2011年第1期12卷 1-12页

作者： Obulkasim, Askar Meijer, Gerrit A. van de Wiel, Mark A. Vrije Univ Amsterdam Med Ctr Dept Epidemiol & Biostat Amsterdam Netherlands Vrije Univ Amsterdam Med Ctr Dept Pathol Amsterdam Netherlands Vrije Univ Amsterdam Dept Math Amsterdam Netherlands

Background: Combining clinical and molecular data types may potentially improve prediction accuracy of a classifier. However, currently there is a shortage of effective and efficient statistical and bioinformatic tools for true integrative data analysis. Existing integrative classifiers have two main disadvantages: First, coarse combination may lead to subtle contributions of one data type to be overshadowed by more obvious contributions of the other. Second, the need to measure both data types for all patients may be both unpractical and (cost) inefficient. Results: We introduce a novel classification method, a stepwise classifier, which takes advantage of the distinct classification power of clinical data and high-dimensional molecular data. We apply classification algorithms to two data types independently, starting with the traditional clinical risk factors. We only turn to relatively expensive molecular data when the uncertainty of prediction result from clinical data exceeds a predefined limit. Experimental results show that our approach is adaptive: the proportion of samples that needs to be re-classified using molecular data depends on how much we expect the predictive accuracy to increase when re-classifying those samples. Conclusions: Our method renders a more cost-efficient classifier that is at least as good, and sometimes better, than one based on clinical or molecular data alone. Hence our approach is not just a classifier that minimizes a particular loss function. Instead, it aims to be cost-efficient by avoiding molecular tests for a potentially large subgroup of individuals;moreover, for these individuals a test result would be quickly available, which may lead to reduced waiting times (for diagnosis) and hence lower the patients distress. Stepwise classification is implemented in R-package stepwiseCM and available at the Bioconductor website.

关键词： Partial Little Square Data Type Random Forest Molecular Data classification algorithm

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