检索结果-内蒙古大学图书馆

Important sampling based active learning for imbalance classification

science China(information sciences) 2020年第8期63卷 196-209页

作者： Xinyue WANG Bo LIU Siyu CAO Liping JING Jian YU School of Computer and Information Technology Beijing Jiaotong University Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University College of Information Science and Technology Hebei Agricultural University

Imbalance in data distribution hinders the learning performance of classifiers. To solve this problem, a popular type of methods is based on sampling(including oversampling for minority class and undersampling for majority class) so that the imbalanced data becomes relatively balanced data. However,they usually focus on one sampling technique, oversampling or undersampling. Such strategy makes the existing methods suffer from the large imbalance ratio(the majority instances size over the minority instances size). In this paper, an active learning framework is proposed to deal with imbalanced data by alternative performing important sampling(ALIS), which consists of selecting important majority-class instances and generating informative minority-class instances. In ALIS, two important sampling strategies affect each other so that the selected majority-class instances provide much clearer information in the next oversampling process, meanwhile the generated minority-class instances provide much more sufficient information for the next undersampling procedure. Extensive experiments have been conducted on real world datasets with a large range of imbalance ratio to verify ALIS. The experimental results demonstrate the superiority of ALIS in terms of several well-known evaluation metrics by comparing with the state-of-the-art methods.

关键词： imbalance classification important sampling active learning oversampling undersampling

来源：评论

学校读者我要写书评

暂无评论

Efficient image representation for object recognition via pivots selection

引用

Frontiers of Computer science 2015年第3期9卷 383-391页

作者： Bojun XIE Yi LIU HuiZHANG Jian YU Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Key Lab of Machine Learning and Computational Intelligence College of Mathematics and Computer Science Hebei University Baoding 071000 China

Patch-level features are essential for achieving good performance in computer vision tasks. Besides well- known pre-defined patch-level descriptors such as scalein- variant feature transform （SIFT） and histogram of oriented gradient （HOG）, the kernel descriptor （KD） method [1] of- fers a new way to ＂grow-up＂ features from a match-kernel defined over image patch pairs using kernel principal compo- nent analysis （KPCA） and yields impressive results. In this paper, we present efficient kernel descriptor （EKD） and efficient hierarchical kernel descriptor （EHKD）, which are built upon incomplete Cholesky decomposition. EKD au- tomatically selects a small number of pivot features for gener- ating patch-level features to achieve better computational effi- ciency. EHKD recursively applies EKD to form image-level features layer-by-layer. Perhaps due to parsimony, we find surprisingly that the EKD and EHKD approaches achieved competitive results on several public datasets compared with other state-of-the-art methods, at an improved efficiency over KD.

关键词： efficient kernel descriptor efficient hierarchi-cal kernel descriptor incomplete Cholesky decomposition,patch-level features image-level features

来源：评论

学校读者我要写书评

暂无评论

Potential Transmission Choice for Internet of Things(IoT):Wireless and Batteryless Communications and Open Problems

引用

China Communications 2021年第2期18卷 241-249页

作者： Zhan Xu Guanjie Hu Minzheng Jia Lan Dong School of Information and Communication Engineering and with Key Laboratory of Modern Measurement&Control Technology Ministry of EducationBeijing Information Science&Technology UniversityChina Beijing Key Lab of Transportation Data Analysis and Mining School of Computer and Information TechnologyBeijing Jiaotong UniversityBeijingChina Department of Information Engineering Beijing Polytechnic CollegeBeijingChina

The 5th generation mobile communications aims at connecting everything and future Internet of Things(IoT)will get everything smartly *** realize it,there exist many *** key challenge is the battery problem for small devices,such as sensors or *** backscatter,also referred to as or battery-free backscatter,is a new potential technology to address this *** early and typical type of batteryless backscatter is ambient ***,batteryless backscatter utilizes environmental wireless signals to enable battery-free devices to communicate with each *** devices first harvest energy from ambient wireless signals and then backscatter these signals so as to transmit their own *** paper reviews the current studies about batteryless backscatter,including various backscatter schemes and theoretical works,and then introduces open problems for future research.

关键词： batteryless backscatter battery-free channel state information(CSI) channel estimation multiple antennas signal detection symbiotic communication

来源：评论

学校读者我要写书评

暂无评论

A mixture language model for class-attribute mining from biomedical literature digital library

A mixture language model for class-attribute mining from bio...

引用

IEEE International Conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Xiaohua Zhou Xiaohua Hu Xiaodan Zhang Daniel D. Wu Data Mining and Bioinformatics Lab College of Information Science & Technology Drexel University Philadelphia PA USA

We define and study a novel text mining problem for biomedical literature digital library, referred to as the class-attribute mining. Given a collection of biomedical literature from a digital library addressing a set of objects (e.g., proteins) and their descriptions (e.g., protein functions), the tasks of class-attribute mining include: (1) to identify and summarize latent classes in the space of objects, (2) to discover latent attribute themes in the space of object descriptions, and (3) to summarize the commonalities and differences among identified classes along each attribute theme. We approach this mining problem through a mixture language model and estimate the parameters of the model using the EM algorithm. We demonstrate the effectiveness of the model with an application called protein community identification and annotation from Medline, the largest biomedical literature digital library with more than 16 millions abstracts.

关键词： Software libraries Proteins data mining Text mining Context modeling bioinformatics Abstracts Educational institutions information science Parameter estimation

来源：评论

学校读者我要写书评

暂无评论

Common latent space identification for heterogeneous co-transfer clustering

引用

Neurocomputing 2017年 269卷 29-39页

作者： Yang, Liu Jing, Liping Liu, Bo Yu, Jian Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing China School of Computer Science and Technology Tianjin University Tianjin China College of Information Science and Technology Agricultural University of Hebei Baoding China

With the rapid development of collection techniques, it is easy to gather various data which come from different domains, such as images, videos, documents, and etc, how to group these heterogeneous data becomes a research issue. Traditional techniques handle these clustering tasks separately, that is one task for one domain, so that they ignore the interactions among domains. In this paper, we present a co-transfer clustering method to deal with these separate tasks together with the aid of co-occurrence data which contain some instances represented in different domains. The proposed method consists of two steps, one is to learn the subspace of different domains that uncovers the latent common topics and respects the intrinsic geometric structure, the next is to simultaneously cluster the instances in all domains via the symmetric nonnegative matrix factorization method. A series of experiments on real-world data sets have shown the performance of the proposed method is better than the state-of-the-art methods. © 2017 Elsevier B.V.

关键词： Co-transfer clustering Collective matrix factorization Heterogeneous feature spaces Symmetric nonnegative matrix factorization

来源：评论

学校读者我要写书评

暂无评论

An improved bat algorithm based on hybrid with ant lion optimizer 13th

An improved bat algorithm based on hybrid with ant lion opti...

引用

13th International Conference on Genetic and Evolutionary Computing, ICGEC 2019

作者： Dao, Thi-Kien Chu, Shu-Chuan Pan, Jeng-Shyang Nguyen, Trong-The Ngo, Truong-Giang Nguyen, Trinh-Dong Tran, Huu-Trung Fujian Provincial Key Lab of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

ISBN: (纸本)9789811533075

Bat Algorithm (BA) is one of the fundamental algorithms for solving optimization problems. However, the BA still exists weaknesses in terms of exploitation and exploration. In this paper, an enhancing capability of exploration and exploitation for BA by hybridizing BA with Ant Lion Optimizer (ALO) is proposed for the global optimization problems. In the experimental section, several benchmark functions are used to test the performance of the proposed approach. Compared results with other algorithms literature show that the proposed method provides a new competitive algorithm. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Global optimization

来源：评论

学校读者我要写书评

暂无评论

Constrained least squares regression for semi-supervised learning

Constrained least squares regression for semi-supervised lea...

引用

18th Pacific-Asia Conference on Advances in Knowledge Discovery and data mining, PAKDD 2014

作者： Liu, Bo Jing, Liping Yu, Jian Li, Jia Beijing Key Lab. of Traffic Data Analysis and Mining Beijing Jiaotong University Beijing 100044 China College of Information Science and Technology Agricultural University of Hebei Hebei 071000 China

ISBN: (纸本)9783319066042

The core tasks of graph based semi-supervised learning (GSSL) are constructing a proper graph and selecting suitable supervisory information. The ideal graph is able to outline the intrinsic data structure, and the ideal supervisory information could represent the whole data. In this paper, we propose a new graph learning method, called constrained least squares regression (CLSR), which integrates the supervisory information into graph learning process. To learn a more adaptive graph, regression coefficients and neighbor relations are combined in CLSR to capture the global and local data structures respectively. Moreover, as byproduct of CLSR, a new strategy is presented to select the high-quality data points as labeled samples, which is practical in real applications. Experimental results on different real world datasets demonstrate the effectiveness of CLSR and the sample selection strategy. © 2014 Springer International Publishing.

关键词： Least squares approximations

来源：评论

学校读者我要写书评

暂无评论

A Novel Under-sampling Method with Gaussian Mixture and Jensen-Shannon Divergence 2022

A Novel Under-sampling Method with Gaussian Mixture and Jens...

引用

4th International Conference on Image, Video and Signal Processing, IVSP 2022

作者： Pan, Xueling Li, Guohe Liu, Shunxin Yu, Qiuyue Li, Ying Beijing Key Lab of Data Mining for Petroleum Data China University of Petroleum Beijing102249 China College of Information Science and Engineering China University of Petroleum Beijing102249 China Department of Computer Science and Technology China University of Petroleum-Beijing at Keramay China

ISBN: (纸本)9781450387415

Due to bias towards major class samples, the traditional classifier cannot obtain excellent performance in imbalanced data. The under-sampling approach is one of the effective methods in imbalance classification. Nowadays, most of the under-sampling methods do not consider the global and local distribution of samples and the intra-class imbalance problem simultaneously. In addition, the sampling quantity cannot be adjusted adaptively in the practical application. To handle the above problems, in this paper, we propose a novel under-sampling approach with the Gaussian mixture model and Jensen-Shannon divergence, called GD-US. Firstly, it utilizes a Gaussian mixture model to cluster the majority class samples. And then, it calculates the number of sampling in each cluster according to its sparsity. In addition, GD-US adopts the global and local distribution of samples to decide the probability of deleting samples. Eventually, the Jensen-Shannon divergence is used to control the number of sampling. The experimental results show that the proposal can help solve the imbalance problem and enhance the classification accuracy. © 2022 ACM.

关键词： data mining

来源：评论

学校读者我要写书评

暂无评论

Frontiers of Artificial Intelligent and Quantitative Management: Preface for ITQM 2024

引用

Procedia Computer science 2024年 242卷 1-8页

作者： Filip, Florin Gheorghe Shi, Yong Pocatilu, Paul Ciurea, Cristian-Eugen Li, Jianping Tien, James M. Berg, Daniel Romanian Academy Information Science and Technology Section Bucharest Romania Research Centre on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing100190 China Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing100190 China College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States Bucharest University of Economic Studies Bucharest Romania College of Engineering University of Miami Coral GablesFL33124 United States

来源：评论

学校读者我要写书评

暂无评论

Advanced information technology and Global Business Competition: Preface for ITQM 2018

引用

Procedia Computer science 2018年 139卷 1-8页

作者： Shi, Yong Wolcott, Peter Kwak, Wikil Chen, Zhengxin Tian, Yingjie Lee, Heeseok College of Information Science and Technology University of Nebraska at Omaha OmahaNE68182 United States School of Economics and Management University of Chinese Academy of Sciences Key Lab of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing100190 China College of Business Administration University of Nebraska at Omaha OmahaNE68182 United States College of Business Korea Advanced Institute Science and Technology Dongdaemum-gu Seoul130-722 Korea Republic of

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：