An overview of complex data stream ensemble classification

2021 ◽  
pp. 1-29
Author(s):  
Xilong Zhang ◽  
Meng Han ◽  
Hongxin Wu ◽  
Muhang Li ◽  
Zhiqiang Chen
Keyword(s):  
Data Streams ◽  
Concept Drift ◽  
Rapid Development ◽  
Complex Structure ◽  
Classification Performance ◽  
Ensemble Classification ◽  
Future Research ◽  
Complex Data ◽  
Advantages And Disadvantages ◽  
Application Fields

With the rapid development of information technology, data streams in various fields are showing the characteristics of rapid arrival, complex structure and timely processing. Complex types of data streams make the classification performance worse. However, ensemble classification has become one of the main methods of processing data streams. Ensemble classification performance is better than traditional single classifiers. This article introduces the ensemble classification algorithms of complex data streams for the first time. Then overview analyzes the advantages and disadvantages of these algorithms for steady-state, concept drift, imbalanced, multi-label and multi-instance data streams. At the same time, the application fields of data streams are also introduced which summarizes the ensemble algorithms processing text, graph and big data streams. Moreover, it comprehensively summarizes the verification technology, evaluation indicators and open source platforms of complex data streams mining algorithms. Finally, the challenges and future research directions of ensemble learning algorithms dealing with uncertain, multi-type, delayed, multi-type concept drift data streams are given.

scholarly journals Efficient Ensemble Classification for Multi-Label Data Streams with Concept Drift

Information ◽  
2019 ◽  
Vol 10 (5) ◽  
pp. 158 ◽  
Author(s):  
Yange Sun ◽  
Han Shao ◽  
Shasha Wang
Keyword(s):  
Real World ◽  
Data Streams ◽  
Concept Drift ◽  
Classification Performance ◽  
Ensemble Classification ◽  
Classification Methods ◽  
Stream Data ◽  
Label Data ◽  
Real World Datasets ◽  
Jensen Shannon Divergence

Most existing multi-label data streams classification methods focus on extending single-label streams classification approaches to multi-label cases, without considering the special characteristics of multi-label stream data, such as label dependency, concept drift, and recurrent concepts. Motivated by these challenges, we devise an efficient ensemble paradigm for multi-label data streams classification. The algorithm deploys a novel change detection based on Jensen–Shannon divergence to identify different kinds of concept drift in data streams. Moreover, our method tries to consider label dependency by pruning away infrequent label combinations to enhance classification performance. Empirical results on both synthetic and real-world datasets have demonstrated its effectiveness.

Study on distributed privacy preserving data mining

2014 ◽  
Vol 11 (2) ◽  
pp. 163-170
Author(s):  
Binli Wang ◽  
Yanguang Shen
Keyword(s):  
Data Mining ◽  
Data Privacy ◽  
Rapid Development ◽  
Privacy Preserving ◽  
Future Research ◽  
Distributed Data ◽  
Distributed Environment ◽  
Privacy Preserving Data Mining ◽  
Advantages And Disadvantages ◽  
Future Research Directions

Recently, with the rapid development of network, communications and computer technology, privacy preserving data mining (PPDM) has become an increasingly important research in the field of data mining. In distributed environment, how to protect data privacy while doing data mining jobs from a large number of distributed data is more far-researching. This paper describes current research of PPDM at home and abroad. Then it puts emphasis on classifying the typical uses and algorithms of PPDM in distributed environment, and summarizing their advantages and disadvantages. Furthermore, it points out the future research directions in the field.

scholarly journals Review of Road Segmentation for SAR Images

2021 ◽  
Vol 13 (5) ◽  
pp. 1011
Author(s):  
Zengguo Sun ◽  
Hui Geng ◽  
Zheng Lu ◽  
Rafał Scherer ◽  
Marcin Woźniak
Keyword(s):  
Rapid Development ◽  
Synthetic Aperture ◽  
Practical Significance ◽  
Future Research ◽  
Sar Images ◽  
The Road ◽  
Advantages And Disadvantages ◽  
Segmentation Methods ◽  
Segmentation Algorithms ◽  
Road Segmentation

Road segmentation for synthetic aperture radar (SAR) images is of great practical significance. With the rapid development and wide application of SAR imaging technology, this problem has attracted much attention. At present, there are numerous road segmentation methods. This paper analyzes and summarizes the road segmentation methods for SAR images over the years. Firstly, the traditional road segmentation algorithms are classified according to the degree of automation and the principle. Advantages and disadvantages are introduced successively for each traditional method. Then, the popular segmentation methods based on deep learning in recent years are systematically introduced. Finally, novel deep segmentation neural networks based on the capsule paradigm and the self-attention mechanism are forecasted as future research for SAR images.

scholarly journals A Review of Data Envelopment Analysis in Airline Efficiency: State of the Art and Prospects

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Qiang Cui ◽  
Li-Ting Yu
Keyword(s):  
Data Envelopment Analysis ◽  
Rapid Development ◽  
Aviation Industry ◽  
Future Research ◽  
Data Envelopment ◽  
Social Concern ◽  
Network Dea ◽  
Advantages And Disadvantages ◽  
Dea Models ◽  
Future Research Directions

The rapid development of the aviation industry has brought about the deterioration of the climate, which makes airline efficiency become a hot issue of social concern. As an important nonparametric method, Data Envelopment Analysis (DEA), has been widely applied in efficiency evaluation. This paper examines 130 papers published in the period of 1993–2020 to summarize the literature involving the special application of DEA models in airline efficiency. The paper begins with an overall review of the existing literature, and then the radial DEA, nonradial DEA, network DEA, dynamic DEA, and DEA models with undesirable outputs applied in airline efficiency are introduced. The main advantages and disadvantages of the above models are summarized, and the drivers of airline efficiency are analyzed. Finally, the literature review ends up with future research directions and conclusions.

scholarly journals Recent Development in X-Ray Imaging Technology: Future and Challenges

Research ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Xiangyu Ou ◽  
Xue Chen ◽  
Xianning Xu ◽  
Lili Xie ◽  
Xiaofeng Chen ◽  
...  
Keyword(s):  
High Performance ◽  
Low Cost ◽  
Rapid Development ◽  
The Body ◽  
Future Research ◽  
X Rays ◽  
X Ray ◽  
Advantages And Disadvantages ◽  
X Ray Imaging ◽  
Imaging Performance

X-ray imaging is a low-cost, powerful technology that has been extensively used in medical diagnosis and industrial nondestructive inspection. The ability of X-rays to penetrate through the body presents great advances for noninvasive imaging of its internal structure. In particular, the technological importance of X-ray imaging has led to the rapid development of high-performance X-ray detectors and the associated imaging applications. Here, we present an overview of the recent development of X-ray imaging-related technologies since the discovery of X-rays in the 1890s and discuss the fundamental mechanism of diverse X-ray imaging instruments, as well as their advantages and disadvantages on X-ray imaging performance. We also highlight various applications of advanced X-ray imaging in a diversity of fields. We further discuss future research directions and challenges in developing advanced next-generation materials that are crucial to the fabrication of flexible, low-dose, high-resolution X-ray imaging detectors.

scholarly journals Research status of damage identification algorithm based on deep learning

2021 ◽  
Vol 233 ◽  
pp. 04039
Author(s):  
Zhu Denghui ◽  
Song Lizhong ◽  
Feng yuan ◽  
Yang Quanshun
Keyword(s):  
Deep Learning ◽  
Target Detection ◽  
Damage Identification ◽  
Rapid Development ◽  
Research Progress ◽  
Data Sets ◽  
Identification Algorithm ◽  
Advantages And Disadvantages ◽  
Detection Technology ◽  
Application Fields

One of the core tasks of computer vision is target detection. With the rapid development of deep learning, target detection technology based on deep learning has become the mainstream algorithm in this field. As one of the main application fields, damage identification has achieved important development in the past decade. This paper systematically summarizes the research progress of damage identification algorithm based on deep learning, analyzes the advantages and disadvantages of each algorithm and its detection results on voc2007, voc2012 and coco data sets. Finally, the main contents of this paper are summarized, and the research prospect of deep learning based damage identification algorithm is prospect.

The Key Technologies for Classification of Distributed Data Streams

2015 ◽  
Vol 727-728 ◽  
pp. 976-981
Author(s):  
Hua Fen Xu ◽  
Jing Wu ◽  
Guo Jun Mao
Keyword(s):  
Data Streams ◽  
Credit Card ◽  
Data Stream ◽  
Large Scale ◽  
Future Research ◽  
Distributed Data ◽  
Data Stream Classification ◽  
Advantages And Disadvantages ◽  
Distributed Data Streams ◽  
Processing Architecture

With advances in data collection and generation technologies, environments that produce data streams is more and more. In recent years, the network application is further universal and the applications of a single data stream transfer toward a multi-node distributed data streams, such as sensor network, network monitoring, web log analysis and the credit card transaction data of multiple sites. These data is not only real-time, continuous and large scale, but also distributed. How to manage and analyze large dynamic datasets is an important subject that researchers are faced with. In view of the situation, it presented the formalization description of homogeneous and heterogeneous distributed data stream in this paper, analyzed advantages and disadvantages of the centralized stream processing architecture and distributed streaming processing architecture, discussed the recent progress in distributed data stream classification algorithm, summed up the problems and challenges faced by the distributed data stream mining, and possible future research directions.

scholarly journals Ensemble based on Accuracy and Diversity Weighting for Evolving Data Streams

2022 ◽  
Author(s):  
Yange Sun ◽  
Han Shao ◽  
Bencai Zhang
Keyword(s):  
Ensemble Learning ◽  
Data Streams ◽  
Concept Drift ◽  
Ensemble Methods ◽  
Current Data ◽  
Ensemble Classification ◽  
Crucial Issue ◽  
Base Classifier ◽  
Real World Applications ◽  
Different Types

Ensemble classification is an actively researched paradigm that has received much attention due to increasing real-world applications. The crucial issue of ensemble learning is to construct a pool of base classifiers with accuracy and diversity. In this paper, unlike conventional data-streams oriented ensemble methods, we propose a novel Measure via both Accuracy and Diversity (MAD) instead of one of them to supervise ensemble learning. Based on MAD, a novel online ensemble method called Accuracy and Diversity weighted Ensemble (ADE) effectively handles concept drift in data streams. ADE mainly uses the following three steps to construct a concept-drift oriented ensemble: for the current data window, 1) a new base classifier is constructed based on the current concept when drift detect, 2) MAD is used to measure the performance of ensemble members, and 3) a newly built classifier replaces the worst base classifier. If the newly constructed classifier is the worst one, the replacement has not occurred. Comparing with the state-of-art algorithms, ADE exceeds the current best-related algorithm by 2.38% in average classification accuracy. Experimental results show that the proposed method can effectively adapt to different types of drifts.

scholarly journals The potential of virtual global mobility: implications for practice and future research

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Jan Selmer ◽  
Michael Dickmann ◽  
Fabian J. Froese ◽  
Jakob Lauring ◽  
B. Sebastian Reiche ◽  
...  
Keyword(s):  
Virtual Work ◽  
Rapid Development ◽  
Theoretical Concept ◽  
Future Research ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Global Mobility ◽  
Communication And Coordination ◽  
Global Work ◽  
International Interactions

PurposeThe COVID-19 pandemic has forced global organizations to adopt technology-driven virtual solutions involving faster, less costly and more effective ways to work worldwide even after the pandemic. One potential outcome may be through virtual global mobility (VGM), defined as the replacement of personal physical international interactions for work purposes with electronic personal online interactions. The purpose of this article is to establish VGM as a theoretical concept and explore to what extent it can replace or complement physical global work assignments.Design/methodology/approachThis perspectives article first explores advantages and disadvantages of global virtual work and then discusses the implementation of VGM and analyses to what extent and how VGM can replace and complement physical global mobility.FindingsRepresenting a change of trend, long-term corporate expatriates could become necessary core players in VGM activities while the increase of the number of global travelers may be halted or reversed. VGM activities will grow and further develop due to a continued rapid development of communication and coordination technologies. Consequently, VGM is here to stay!Originality/value The authors have witnessed a massive trend of increasing physical global mobility where individuals have crossed international borders to conduct work. The authors are now observing the emergence of a counter-trend: instead of moving people to their work the authors often see organizations moving work to people. This article has explored some of the advantages, disadvantages, facilitators and barriers of such global virtual work. Given the various purposes of global work the authors chart the suitability of VGM to fulfill these organizational objectives.

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