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Research on Star/Galaxy Classification Based on Stacking Ensemble Learning

Chinese Astronomy and Astrophysics 2020年第3期44卷 345-355页

作者： Chao, L.I. Wen-hui, ZHANG Ran, L.I. Jun-yi, W.A.N.G. Ji-ming, L.I.N. College of Information and Communication Engineering Guilin University of Electronic Technology Guilin 541004 Key Laboratory of Education Ministry for Cognitive Radio and Information Processing Guilin University of Electronic Technology Guilin 541004 Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Cloud Computing and Complex Systems Guilin University of Electronic Technology Guilin 541004 Guangxi Key Laboratory for Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin 541004 Guangxi College and University Key Laboratory for Satellite Navigation and Position Sensing Guilin 541004

Machine learning has achieved great success in many areas today, but the forecast effect of machine learning often depends on the specific problem. An ensemble learning forecasts results by combining multiple base classifiers. Therefore, its ability to adapt to various scenarios is strong, and the classification accuracy is high. In response to the low classification accuracy of the darkest source magnitude set of stars/galaxies in the Sloan Digital Sky Survey (SDSS), a star/galaxy classification algorithm based on the stacking ensemble learning is proposed in this paper. The complete photometric data set is obtained from the SDSS data Release (DR) 7, and divided into the bright source magnitude set, dark source magnitude set, and darkest source magnitude set according to the stellar magnitude. Firstly, the 10-fold nested cross-validation method is used for the darkest source magnitude set, then the Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms are used to establish the base-classifier model;the Gradient Boosting Decision Tree (GBDT) is used as the meta-classifier model. Finally, based on the classification accuracy of galaxies and other indicators, the classification results are analyzed and compared with the results obtained by the Function Tree (FT), SVM, RF, GBDT, Stacked Denoising Autoencoders (SDAE), Deep Belief Nets (DBN), and Deep Perception Decision Tree (DPDT) models. The experimental results show that the stacking ensemble learning model has improved the classification accuracy of galaxies in the darkest source magnitude set by nearly 10% compared to the function tree algorithm. Compared with other traditional machine learning algorithm, stronger lifting algorithm, and deep learning algorithm, the stacking ensemble learning model also has different degrees of improvement. © 2020

关键词： Galaxies: fundamental parameters methods: data analysis Stars: fundamental parameters techniques: photometric

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Using Rough Sets to Improve the High-dimensional data Anomaly Detection Method Based on Extended Isolation Forest

Using Rough Sets to Improve the High-dimensional Data Anomal...

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International Conference on Computer Supported Cooperative Work in Design

作者： Hanlin Liu Jiantao Zhou Hua Li College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Anomaly detection refers to the identification of data objects that deviate from the general data distribution. One of the important challenges in anomaly detection is handling high-dimensional data, especially when it contains a large number of redundant attributes. Although traditional Isolation Forest (iForest) and Extended Isolation Forest (EIF) have performed well in anomaly detection, the detection accuracy is still extremely influenced by redundant attributes. In this paper, we propose a two-tier architecture named RS-EIF, to alleviate the negative impact of redundant attributes. Specifically, the RS-EIF combines the rough set theory and EIF algorithm and provides an effective basis for the EIF-based anomaly detection method by removing redundant attributes with rough set theory. Experimental results show that the RS-EIF algorithm performs better than the iForest and EIF algorithms in terms of anomaly detection rate, and the results of the combination of rough set theory and EIF are more prominent compared to the other combination methods.

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EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced Graph Sample and Aggregate Inductive Learning Algorithm

EG-KGR: A Knowledge Graph Reasoning Model Based on Enhanced ...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Yuejia Wu Jian-Tao Zhou Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Hohhot China

Knowledge Graph is an important research field that involves the storage and management of knowledge, but the incompleteness and sparsity of Knowledge Graphs hinder their application in many fields. Knowledge Graph Reasoning aims to alleviate this problem by completing missing paths or identifying wrong paths between entities. Graph Convolution Network (GCN) based methods are one of the state-of-the-art approaches to this work. However, it is difficult to directly generalize to unknown nodes and utilizes valid information from the local neighborhood which results in poor flexibility and extensibility and will loss of important information. This paper presents EG-KGR, a plug-and-play knowledge reasoning model based on enhanced graph sampling and aggregate inductive learning algorithm to relieve the above problems and enhance existing GCN-based methods. Specifically, EG-KGR supports incremental characteristics, uses inductive learning to replace transductive learning, and designs random sampling and local information sampling optimization methods to improve the model's generalization ability, prediction accuracy, and running speed. Extensive experimental results show that our EG-KGR can achieve optimal results.

关键词： Training Knowledge engineering Convolution Aggregates Optimization methods Knowledge graphs Learning (artificial intelligence)

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The Component data for E-portfolio

The Component Data for E-portfolio

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2013教育技术与信息系统国际会议

作者： Yanxia Pang Jumei Zou Shaochan Dong Academy of Computer and information Shanghai Second Polytechnic University Department of Education Information Technology Hangzhou Normal University Cloud Computing and Big Data Center Academy of Software East China Normal University

We introduce the component data for E-portfolio(Electronic portfolio). E-portfolio) is used to cllect works of students and aimed at learning evaluation, so the components of E-portfolio must be standardized. We do some research on the component data structure of E-portfolio. Based on the features of E-portfolio we use xml file as the storage format of E-portfolio. Thus we can define xsd file which is the structure file of xml file for the users. People can build their E-portfolio on the structure xsd file. In this way E-portfolio based on xsd structure are in same format. That helps for further E-portfolio share and evaluation.

关键词： E-portfolio component data xsd standard evaluation

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A Graph Sequence Generator and Multi-head Self-attention Mechanism based Knowledge Graph Reasoning Architecture

A Graph Sequence Generator and Multi-head Self-attention Mec...

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International Conference on Computer Supported Cooperative Work in Design

作者： Yuejia Wu Jian-Tao Zhou College of Computer Science Inner Mongolia University National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Engineering Research Center of Ecological Big Data Ministry of Education Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Key Laboratory of Discipline Inspection and Supervision Big Data Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Hohhot China

Knowledge Graph (KG) is an essential research direction that involves storing and managing knowledge data, but its incompleteness and sparsity hinder its development in various applications. Knowledge Graph Reasoning (KGR) is an effective method to solve this limitation via reasoning missing knowledge based on existing knowledge. The graph Convolution Network (GCN) based method is one of the state-of-the-art approaches to this work. However, there are still some problems, such as the insufficient ability to perceive graph structure and the poor effect of learning data features which may limit the reasoning accuracy. This paper proposes a KGR architecture based on a graph sequence generator and multi-head self-attention mechanism, named GaM-KGR, to improve the above problems and enhance prediction accuracy. Specifically, the GaM-KGR first introduces the graph generation model into the field of KGR for graph representation learning to obtain the hidden features in the data so that enhancing the reasoning effect and then embeds the generated graph sequence into the multi-head self-attention mechanism for subsequent processing to improve the graph structure perception ability of the proposed architecture, so that it can process the graph structure data more appropriately. Extensive experimental results show that the GaM-KGR architecture can achieve the state-of-the-art prediction results of current GCN-based models.

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A Time-Efficient Distributed Constant Conditional Functional Dependency Discovery Algorithm for data Consistency

A Time-Efficient Distributed Constant Conditional Functional...

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IEEE Annual International Computer Software and Applications Conference (COMPSAC)

作者： Junchao Chen Jian-tao Zhou Xinyu Hao Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology College of Computer Science Inner Mongolia University Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian Hohhot China

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

The accuracy of data analysis depends on data quality, and addressing data consistency issues is a key challenge to improve it. Constant Conditional Functional Dependency (CCFD) is an effective approach that ensures data consistency by enforcing bindings of semantically related values, thus providing quality assurance for data analysis and decision-making processes. However, with the growth of data scale, especially the increasing number of data tuples and attributes, existing single-machine CCFD discovery algorithms face issues of low computational efficiency and lengthy computation time. This paper proposes a time-efficient distributed CCFD discovery algorithm (DCCFD). Through the optimization of data preprocessing and index mapping, the data organization structure is enhanced, laying the foundation for the discovery of CCFDs under distributed conditions. The Spark parallel computing framework is used to partition the dataset, which accelerates the parallel loading and processing of data. Additionally, this algorithm ensures accuracy and processing speed when discovering dependencies by efficiently generating frequent itemsets and verifying CCFDs in parallel. Experiments on multiple real datasets show that, especially with the complex Airline dataset, the DCCFD algorithm not only accurately discovers CCFDs, but also reduces the average running time by 75.64% compared with the preCFDMiner algorithm.

关键词： Accuracy data preprocessing Software algorithms Distributed databases Parallel processing Software Partitioning algorithms

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Recommending more suitable music based on users’ real context 14th

Recommending more suitable music based on users’ real conte...

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14th EAI International Conference on Collaborative computing: Networking, Applications and Worksharing, CollaborateCom 2018

作者： Yang, Qing Zhan, Le Han, Li Zhang, Jingwei Bi, Zhongqin School of Electronic Engineering and Automation Guilin University of Electronic Technology Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin541004 China College of Computer Science and Technology Shanghai University of Electronic Power Shanghai200090 China

ISBN: (纸本)9783030129804

Music recommendation is an popular function for personalized services and smart applications since it focuses on discovering users’ leisure preference. The traditional music recommendation strategy captured users’ music preference by analyzing their historical behaviors to conduct personalized recommendation. However, users’ current states, such as in busy working or in a leisure travel, etc., have an important influence on their music enjoyment. Usually, those existing methods only focus on pushing their favorite music to users, which may be not the most suitable for current scenarios. Users’ current states should be taken into account to make more perfect music recommendation. Considering the above problem, this paper proposes a music recommendation method by considering both users’ current states and their historical behaviors. First, a feature selection process based on ReliefF method is applied to discover the optimal features for the following recommendation. Second, we construct different feature groups according to the feature weights and introduce Naive Bayes model and Adaboost algorithm to train these feature groups, which will output a base classifier for each feature group. Finally, a majority voting strategy decides the optimal music type and each user will be recommended more suitable music based on their current context. The experiments on the real datasets show the effectiveness of the proposed method. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

关键词： Adaptive boosting

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Multi-Scale Region-based Fully Convolutional Networks

Multi-Scale Region-based Fully Convolutional Networks

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IEEE International Conference on Power, Intelligent computing and Systems (ICPICS)

作者： Chengqi Xu Xuehai Hong Yuanzhou Yao Hengheng Shen Qian Ma Hui Jiang Big data research institute Cloud Computing Center Chinese Academy of Sciences Shangrao China Institute of Computing Technology Chinese Academy of Sciences Beijing China College of Information Engineering Sichuan Agricultural University Ya’ an City Sichuan State China

ISBN: (数字)9781728198743

ISBN: (纸本)9781728198750

A multi-scale R-FCN detection algorithm is presented to solve the problem of region-based full convolution network (R-FCN) in multi-scale object detection. Firstly, in order to solve the problem that R-FCN algorithm has limited receptive field and semantic information in single feature map detection, a multi-scale proposal box (proposal) is obtained by using multi-scale feature maps in the main network. At the same time, in order to make the feature maps of different layers have rich semantic information at the same time during the two-stage detection, the feature maps of different layers are fused from top to bottom. Finally, in order to make the two-stage position-sensitive score map also have good multiscale representation ability, a multi-layer shared convolution method is used to generate the position-sensitive score map. The experimental results on PASCAL VOC dataset show that the multiscale R-FCN algorithm in this paper is better than the original R-FCN algorithm in detection accuracy. At the same time, the detection map shows that the algorithm performs better in multi-scale object detection.

关键词： Feature extraction Object detection Proposals Semantics Detection algorithms Convolution Entropy

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A Blockchain-Based Multi-cloud Storage data Consistency Verification Scheme

A Blockchain-Based Multi-Cloud Storage Data Consistency Veri...

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IEEE International Conference on big data and cloud computing (Bdcloud)

作者： Feiyu Wang Jian-tao Zhou Hao Wang Xu Guo College of Computer Science Inner Mongolia University Hohhot China Inner Mongolia Engineering Laboratory for Cloud Computing and Service Software Inner Mongolia Key Laboratory of Social Computing and Data Processing Inner Mongolia Engineering Laboratory for Big Data Analysis Technology Engineering Research Center of Ecological Big Data Ministry of Education National & Local Joint Engineering Research Center of Intelligent Information Processing Technology for Mongolian China

Users worldwide widely use cloud storage because of its efficiency, convenience, and high availability. Multi-cloud storage is usually selected to ensure the high availability of data. Unfortunately, when data is migrated and replicated between multi-cloud data centers, it is not easy to guarantee data con-sistency. This paper proposes an efficient, secure, and new data consistency verification scheme using blockchain technology. In order to reduce the computation and communication overhead in the verification process, our scheme uses encrypted tags to build Merkle hash tree to generate unique and lightweight verification proofs and does not use third-party auditors. The final theoretical and experimental analysis shows that our scheme has higher security and a faster verification process in multi-cloud storage.

关键词： cloud computing data centers Costs Blockchains Behavioral sciences

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OpenSim: A Simulator of OpenStack Services

OpenSim: A Simulator of OpenStack Services

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Asia International Conference on Modeling & Simulation

作者： Dinkar Sitaram Phalachandra Hallymysore Sudheendra Harwalkar Supriya Kathare Anirudh Srinivasan Archana Reddy Konark Kumar Sahana Sekhar Department of Computer Science and Engineering PES University Center for Cloud Computing and Big Data Bangalore India Dept. of Comput. Sci. & Eng. PES Univ. Bangalore Bangalore India

cloud computing is still an emerging field experiencing rapid advancement in both industry and academia. cloud computing has solved many problems initially faced by internet application developers such as acquisition of a server with a fixed capacity to handle expected peak application demand which led to under-utilization of provisioned resources, by enabling a new consumption and delivery model for IT services. It involves provisioning of dynamically scalable and virtualized resources as a service over the Internet. Evaluation of algorithms and policies in an exhaustive manner directly on a cloud is infeasible as it involves a lot of time and effort. Moreover, utilization of real cloud infrastructure limits the experiment to the scale of the infrastructure. A desirable alternative would be utilization of simulation tools that enables evaluation of a hypothesis in a repeatable, controlled and timely manner prior to software development in a cloud environment. Taking into consideration these issues and the ever-growing popularity of OpenStack, in this paper we propose OpenSim a simulator of OpenStack services. It has been developed by the authors of this paper with the intention of simulating new algorithms and also to enable a user to study the behavior of an application running under various deployment configuration in an OpenStack environment.

关键词： Virtual machining Servers Computational modeling cloud computing Neutrons IP networks Scheduling algorithms

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