检索结果-内蒙古大学图书馆

International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Mohamed Jaward Bah Ji Zhang Ting Yu Feng Xia Zhao Li Shuigeng Zhou Hongzhi Wang Big Data Intelligence Research Center Zhejiang Lab Hangzhou China University of Southern Queensland School of Sciences Toowoomba Australia Institute of Innovation Science and Sustainability Federation University Australia Ballarat Australia Zhejiang University China Fudan University School of Computer Science Shanghai China Harbin Institute of Technology Massive Data Computing Lab China

Outlier detection is an important data mining task, and developing effective methods to detect outliers is challenging in cases where there is insufficient labeled data. Manually labeling the data is labor-intensive and time-consuming. Because of a limited number of labeled samples, the classes are unbalanced, resulting in a class-imbalance problem. Existing methods fail to address these aforementioned issues holistically and fall short in generating quality outlier samples for effective outlier detection accuracy. In this paper, we propose a new solution that tackles these problems. We propose a. Generative Adversarial Active Learning method (DIR-GAAL), which generates Diverse, Informative, and Representative outlier samples through active learning, and employs the mini-max game between the generator and discriminator in a generative adversarial network. We conducted extensive experiments on several benchmark datasets to evaluate the performance of our method. When compared to other benchmark methods, our method consistently demon-strates better outlier detection accuracy without being negatively affected by the class-imbalance problem.

关键词： Learning systems Games Benchmark testing Generative adversarial networks Generators Labeling Task analysis

来源：评论

学校读者我要写书评

暂无评论

PCP theorems, SETH and more: Towards proving sub-linear time inapproximability

arXiv

引用

arXiv 2020年

作者： Ma, Hengzhao Li, Jianzhong Research Center of Massive Data Computing Harbin Institute of Technology Nangang HarbinHeilongjiang China

In this paper we propose the PCP-like theorem for sub-linear time inapproximability. Abboud et al. have devised the distributed PCP framework for sub-quadratic time inapproximability. We show that the distributed PCP theorem can be generalized for proving arbitrary polynomial time inapproximability, but fails in the linear case. We prove the sub-linear PCP theorem by adapting from an MA-protocol for the Set Containment problem, and show how to use the theorem to prove both existing and new inapproximability results, exhibiting the power of the sub-linear PCP theorem. Considering the emerging research works on sub-linear time algorithms, the sub-linear PCP theorem is important in guiding the research in sub-linear time approximation algorithms. Copyright © 2020, The Authors. All rights reserved.

关键词： Polynomial approximation

来源：评论

学校读者我要写书评

暂无评论

SFSC: Segment feature sampling classifier for time series classification 4th

SFSC: Segment feature sampling classifier for time series cl...

引用

4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Meng, Fanshan Yue, Tianbai Wang, Hongzhi Gao, Hong Li, Yaping Massive Data Computing Research Center Harbin Institute of Technology Xidazhijie. 92 Harbin China

ISBN: (纸本)9789811322020

Time series classification research is important in data mining. However, the existing methods are not fast and accurate enough. Common classification algorithms cannot satisfy the requirements of time series classification. Since the dimensions of each time series sample may be different. Based on this character, we propose a new approach combining the feature sampling algorithm and the random forest classifier (SFSC). To test the efficiency and effectiveness of SFSC, UCR time series datasets are used for our experiments. We also discuss the performance of Dynamic Time Warping (DTW) compared with our approach. We conclude the suitable situations in which our method behaves better. Besides, We discuss the method to determine the best parameters for feature sampling method. The experiment results shows that in most cases, the SFSC algorithm behaves better than DTW and can be used for real-time query and large dataset. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Time series

来源：评论

学校读者我要写书评

暂无评论

Statistical learning-based prediction of execution time of data-intensive program under hadoop2.0 4th

Statistical learning-based prediction of execution time of d...

引用

4th International Conference of Pioneer Computer Scientists, Engineers and Educators, ICPCSEE 2018

作者： Zhang, Haoran Li, Jianzhong Wang, Hongzhi Massive Data Computing Research Center Harbin Institute of Technology Xidazhijie. 92 Harbin China

ISBN: (纸本)9789811322020

This paper is mainly to predict the running time of data-intensive MapReduce program under Hadoop2.0 environment. Although MapReduce programs are diverse, they can be divided into data-intensive and computationally intensive, depending on the time complexity and the nature of the program. The prediction of computationally intensive programs has always been difficult, and Hadoop has exhibited certain database attributes that are basically data-intensive. Moreover, the relationship between data-intensive programs and the amount of data is more closely related and shows certain statistical characteristics. So the method of statistical learning is applied to predict the execution time. This paper first generates training data and test data according to requirements, and then selects the appropriate features through the analysis of the logs. The prediction was first performed using the KCCA algorithm. However, the deficiencies were found. Then based on the characteristics of the kernel function, a prediction method based on deep learning was proposed, and the result was significant. © Springer Nature Singapore Pte Ltd. 2018.

关键词： MapReduce

来源：评论

学校读者我要写书评

暂无评论

SFSC: Segment Feature Sampling Classifier for Time Series Classification

SFSC: Segment Feature Sampling Classifier for Time Series Cl...

引用

2018国际计算机前沿大会（原国际青年计算机大会）

作者： Fanshan Meng Tianbai Yue Hongzhi Wang Hong Gao Yaping Li Massive Data Computing Research Center Harbin Institute of Technology

Time series classification research is important in data mining. However, the existing methods are not fast and accurate *** classification algorithms cannot satisfy the requirements of time series classification. Since the dimensions of each time series sample may be different. Based on this character, we propose a new approach combining the feature sampling algorithm and the random forest classifier(SFSC). To test the efficiency and effectiveness of SFSC, UCR time series datasets are used for our experiments. We also discuss the performance of Dynamic Time Warping(DTW) compared with our approach. We conclude the suitable situations in which our method behaves ***, We discuss the method to determine the best parameters for feature sampling method. The experiment results shows that in most cases, the SFSC algorithm behaves better than DTW and can be used for real-time query and large dataset.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Statistical Learning-Based Prediction of Execution Time of data-Intensive Program Under Hadoop2.0

Statistical Learning-Based Prediction of Execution Time of D...

引用

2018国际计算机前沿大会（原国际青年计算机大会）

作者： Haoran Zhang Jianzhong Li Hongzhi Wang Massive Data Computing Research Center Harbin Institute of Technology

This paper is mainly to predict the running time of dataintensive Map Reduce program under Hadoop2.0 environment. Although Map Reduce programs are diverse, they can be divided into data-intensive and computationally intensive, depending on the time complexity and the nature of the program. The prediction of computationally intensive programs has always been difficult, and Hadoop has exhibited certain database attributes that are basically data-intensive. Moreover, the relationship between data-intensive programs and the amount of data is more closely related and shows certain statistical characteristics. So the method of statistical learning is applied to predict the execution time. This paper first generates training data and test data according to requirements, and then selects the appropriate features through the analysis of the logs. The prediction was first performed using the KCCA algorithm. However, the deficiencies were found. Then based on the characteristics of the kernel function, a prediction method based on deep learning was proposed, and the result was significant.

关键词： Training data Feature extraction KCCA Deep learning

来源：评论

学校读者我要写书评

暂无评论

Disease Prediction Based on Transfer Learning in Individual Healthcare

Disease Prediction Based on Transfer Learning in Individual ...

引用

The Third International Conference of Pioneering Computer Scientists, Engineers and Educators,ICPCSEE 2017(originally ICYCSEE)

作者： Yang Song Tianbai Yue Hongzhi Wang Jianzhong Li Hong Gao Massive Data Computing Research Center Harbin Institute of Technology Heilongjiang University

Nowadays, emerging mobile medical technology and disease prevention become new trends of disease prevention and control. Based on this technology, we present disease prediction models based on transfer learning. Breast cancer disease data has been used to build our *** to the neural networks, the basic model has been *** unlabeled data, transfer learning is a appropriate way to revise the module to increase accuracy. The test results show that the algorithm is suitable for data classification, especially for unlabeled health data.

关键词： Individual healthcare Transfer learning Neural networks Disease prediction Unlabeled data

来源：评论

学校读者我要写书评

暂无评论

An Energy-Efficient data Placement Algorithm and Node Scheduling Strategies in Cloud computing Systems

An Energy-Efficient Data Placement Algorithm and Node Schedu...

引用

2013 2nd International Conference on Advances in Computer Science and Engineering(CSE 2013)

作者： Yanwen Xiao Jinbao Wang Yaping Li Hong Gao Massive Data Computing Research Lab Harbin Institute of Technology HIT Internet & Information Center Harbin Institute of Technology HIT

With the rise of the cloud computing, saving energy consumed by cloud systems has become a tricky issue nowadays. How to place data efficiently and schedule the nodes effectively in a cloud platform are very important issues from the view of the energy-saving. However, the state-of-the-art node-scheduling strategies can't save large amount of energy for the cloud computing platforms significantly. This paper proposes a heuristic data placement algorithm and two node scheduling strategies for cloud platforms to save energy with tasks guaranteed. The Cloudsim is employed to simulate a private cloud system. Energy-saving is achieved by turning on minimum nodes to cover maximum data blocks. The problem of covering data block with computing nodes is abstracted as a set cover problem, and a greedy algorithm is utilized to solve this problem. This approach is practical to any cloud computing infrastructure. The designed experiment verifies the efficiency of the data placement algorithm and node scheduling strategies proposed in this paper.

关键词： cloud computing data placement algorithm nodescheduling strategies energy efficiency

来源：评论

学校读者我要写书评

暂无评论

Generating Power-Efficient Query Execution Plan

Generating Power-Efficient Query Execution Plan

引用

2013 2nd International Conference on Advances in Computer Science and Engineering(CSE 2013)

作者： Xiaowei Liu Jinbao Wang Haijie Wang Hong Gao Massive Data Computing Research Lab Harbin Institute of Technology HIT Internet & Information Center Harbin Institute of Technology HIT

As more and more power is consumed in servers, growing attention has been paid to power management. However, little work has been dated on the power-efficiency of query execution plans generated by the DBMS. Traditional optimal query execution plan does not take power-efficiency into account. This paper tries to generate a power-efficient query execution plan by redesigning the query optimizer of the database. We build a power model to estimate the power consumption of a query execution plan and propose an algorithm to generate the power-efficient query execution plan. Using two experiments based on the TPC-H benchmark, we evaluate the accuracy of our power model and compare the power-efficient query execution plan with the traditional optimal query execution plan.

关键词： query execution plan power-efficiency power model

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：