Change search
Refine search result
1 - 13 of 13
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Ma, Liyao
    et al.
    Univ Jinan, CHI.
    Sun, Bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies. Blekinge Inst Technol, Dept Creat Technol, Karlskrona, Sweden..
    Han, Chunyan
    Univ Jinan, CHI.
    Learning Decision Forest from Evidential Data: the Random Training Set Sampling Approach2017In: International Conference on Systems and Informatics, IEEE , 2017, p. 1423-1428Conference paper (Refereed)
    Abstract [en]

    To learn decision trees from uncertain data modelled by mass functions, the random training set sampling approach for learning belief decision forests is proposed. Given an uncertain training set, a collection of simple belief decision trees are trained separately on each corresponding new set drawn by random sampling from the original one. Then the final prediction is made by majority voting. After discussing the selection of parameters for belief decision forests, experiments on Balance scale data are carried on for performance validation. Results show that with different kinds of uncertainty, the proposed method guarantees an obvious improvement in classification accuracy.

  • 2.
    Ma, Liyao
    et al.
    University of Jinan, CHN .
    Sun, Bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Han, Chunyan
    University of Jinan, CHN .
    Training Instance Random Sampling Based Evidential Classification Forest Algorithms2018In: 2018 21st International Conference on Information Fusion, FUSION 2018, Institute of Electrical and Electronics Engineers Inc. , 2018, p. 883-888Conference paper (Refereed)
    Abstract [en]

    Modelling and handling epistemic uncertainty with belief function theory, different ways to learn classification forests from evidential training data are explored. In this paper, multiple base classifiers are learned on uncertain training subsets generated by training instance random sampling approach. For base classifier learning, with the tool of evidential likelihood function, gini impurity intervals of uncertain datasets are calculated for attribute splitting and consonant mass functions of labels are generated for leaf node prediction. The construction of gini impurity based belief binary classification tree is proposed and then compared with C4.5 belief classification tree. For base classifier combination strategy, both evidence combination method for consonant mass function outputs and majority voting method for precise label outputs are discussed. The performances of different proposed algorithms are compared and analysed with experiments on VCI Balance scale dataset. © 2018 ISIF

  • 3.
    Ma, Liyao
    et al.
    University of Jinan, CHI.
    Sun, Bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Li, Ziyi
    University of Science and Technology of China, CHI.
    Bagging likelihood-based belief decision trees2017In: 20th International Conference on Information Fusion, Fusion 2017: Proceedings, Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 321-326, article id 8009664Conference paper (Refereed)
    Abstract [en]

    To embed ensemble techniques into belief decision trees for performance improvement, the bagging algorithm is explored. Simple belief decision trees based on entropy intervals extracted from evidential likelihood are constructed as the base classifiers, and a combination of individual trees promises to lead to a better classification accuracy. Requiring no extra querying cost, bagging belief decision trees can obtain good classification performance by simple belief tree combination, making it an alternative to single belief tree with querying. Experiments on UCI datasets verify the effectiveness of bagging approach. In various uncertain cases, the bagging method outperforms single belief tree without querying, and is comparable in accuracy to single tree with querying. © 2017 International Society of Information Fusion (ISIF).

  • 4.
    Sun, Bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Automated Traffic Time Series Prediction2018Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    Intelligent transportation systems (ITS) are becoming more and more effective. Robust and accurate short-term traffic prediction plays a key role in modern ITS and demands continuous improvement. Benefiting from better data collection and storage strategies, a huge amount of traffic data is archived which can be used for this purpose especially by using machine learning.

    For the data preprocessing stage, despite the amount of data available, missing data records and their messy labels are two problems that prevent many prediction algorithms in ITS from working effectively and smoothly. For the prediction stage, though there are many prediction algorithms, higher accuracy and more automated procedures are needed.

    Considering both preprocessing and prediction studies, one widely used algorithm is k-nearest neighbours (kNN) which has shown high accuracy and efficiency. However, the general kNN is designed for matrix instead of time series which lacks the use of time series characteristics. Choosing the right parameter values for kNN is problematic due to dynamic traffic characteristics. This thesis analyses kNN based algorithms and improves the prediction accuracy with better parameter handling using time series characteristics.

    Specifically, for the data preprocessing stage, this work introduces gap-sensitive windowed kNN (GSW-kNN) imputation. Besides, a Mahalanobis distance-based algorithm is improved to support correcting and complementing label information. Later, several automated and dynamic procedures are proposed and different strategies for making use of data and parameters are also compared.

    Two real-world datasets are used to conduct experiments in different papers. The results show that GSW-kNN imputation is 34% on average more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%. The Mahalanobis distance-based models efficiently correct and complement label information which is then used to fairly compare performance of algorithms. The proposed dynamic procedure (DP) performs better than manually adjusted kNN and other benchmarking methods in terms of accuracy on average. What is better, weighted parameter tuples (WPT) gives more accurate results than any human tuned parameters which cannot be achieved manually in practice. The experiments indicate that the relations among parameters are compound and the flow-aware strategy performs better than the time-aware one. Thus, it is suggested to consider all parameter strategies simultaneously as ensemble strategies especially by including window in flow-aware strategies.

    In summary, this thesis improves the accuracy and automation level of short-term traffic prediction with proposed high-speed algorithms.

  • 5.
    Sun, Bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    How to Learn Research Ethics Regarding External Validity2017Report (Other academic)
    Abstract [en]

    There are some overlaps between external validity and research ethics. For example, how to handle raw data so that they can be processed by some algorithms and methods while making sure the handling is not misconducted. Or, to what extent the data should be shared to allow the readers to replicate the experiments while keeping sensitive data credential. To understand those problems, this work firstly presents several alternative methods, then uses a combined systematic process to analyse several cases. We can see that one problem often has more than one solution and they should be carefully considered to select a suitable one if any. The combined process is working well and should be considered to engage when analysing research ethical problems.

  • 6.
    Sun, Bin
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Toward Automatic Data-Driven Traffic Time Series Prediction2017In: 5th Swedish Workshop on Data Science, 2017, Vol. 12, article id 12Conference paper (Refereed)
    Abstract [en]

    Short-term traffic prediction on freeways has been an active research subject in the past several decades. Various algorithms covering a broad range of topics regarding performance, data requirements and efficiency have been proposed. However, the implementation of machine learning based algorithms in traffic management centres is still limited. Two main reasons for this situation are, the data is messy or missing, and the parameter tuning requires experienced engineers.

    The main objective of this thesis was to develop a procedure that can improve the performance and automation level of short-term traffic prediction.

    Missing data is a problem that prevents many prediction algorithms in ITS from working effectively. Much work has been done to impute those missing data. Among different imputation methods, k-nearest neighbours (kNN) has shown excellent accuracy and efficiency. However, the general kNN is designed for matrix instead of time series so it lacks the usage of time series characteristics such as windows and weights that are gap-sensitive. We introduce gap-sensitive windowed kNN (GSW-kNN) imputation for time series. The results show that GSW-kNN is 34% more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%.

    Lacking accurate accident information (labels) is another problem that prevents huge amount of traffic data to be fully used. We improve a Mahalanobis distance based algorithm to be able to handle differential data to estimate flow fluctuations and detect accidents and use it to support correcting and complementing accident information. The outlier detection algorithm provides accurate suggestions for accident occurring time, duration and direction. We also develop a system with interactive user interface to realize this procedure. There are three contributions for data handling. Firstly, we propose to use multi-metric traffic data instead of single metric for traffic outlier detection. Secondly, we present a practical method to organise traffic data and to evaluate the organisation for Mahalanobis distance. Thirdly, we describe a general method to modify Mahalanobis distance algorithms to be updatable.

    For automatic parameter tuning, the experiments show that the flow-aware strategy performs better than the time-aware one. Thus, we use all parameter strategies simultaneously as ensemble strategies especially by including window in flow-aware strategies.

    Based on the above studies, we have developed online-orientated and offline-orientated algorithms for real-time traffic forecasting. The online automatic tuned version is performing near the optimal manual tuned performance. The offline version gives the performance that cannot be achieved using the manual tuning. It is also 3.05% better than XGB and 11.7% better than traditional SARIMA.

  • 7.
    Sun, Bin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Cheng, Wei
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Bai, Guohua
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Goswami, Prashant
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Correcting and complementing freeway traffic accident data using mahalanobis distance based outlier detection2017In: Technical Gazette, ISSN 1330-3651, E-ISSN 1848-6339, Vol. 24, no 5, p. 1597-1607Article in journal (Refereed)
    Abstract [en]

    A huge amount of traffic data is archived which can be used in data mining especially supervised learning. However, it is not being fully used due to lack of accurate accident information (labels). In this study, we improve a Mahalanobis distance based algorithm to be able to handle differential data to estimate flow fluctuations and detect accidents and use it to support correcting and complementing accident information. The outlier detection algorithm provides accurate suggestions for accident occurring time, duration and direction. We also develop a system with interactive user interface to realize this procedure. There are three contributions for data handling. Firstly, we propose to use multi-metric traffic data instead of single metric for traffic outlier detection. Secondly, we present a practical method to organise traffic data and to evaluate the organisation for Mahalanobis distance. Thirdly, we describe a general method to modify Mahalanobis distance algorithms to be updatable. © 2017, Strojarski Facultet. All rights reserved.

  • 8.
    Sun, Bin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Cheng, Wei
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Goswami, Prashant
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Bai, Guohua
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Short-Term Traffic Forecasting Using Self-Adjusting k-Nearest Neighbours2018In: IET Intelligent Transport Systems, ISSN 1751-956X, E-ISSN 1751-9578, Vol. 12, no 1, p. 41-48Article in journal (Refereed)
    Abstract [en]

    Short-term traffic forecasting is becoming more important in intelligent transportation systems. The k-nearest neighbours (kNN) method is widely used for short-term traffic forecasting.However, kNN parameters self-adjustment has been a problem due to dynamic traffic characteristics. This paper proposes a fully automatic dynamic procedure kNN (DP-kNN) that makes the kNN parameters self-adjustable and robust without predefined models or training. We used realworld data with more than one-year traffic records to conduct experiments. The results show that DP-kNN can perform better than manually adjusted kNN and other benchmarking methods with regards to accuracy on average. This study also discusses the difference between holiday and workday traffic prediction as well as the usage of neighbour distance measurement.

  • 9.
    Sun, Bin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Liyao, Ma
    University of Jinan, CHI.
    Wei, Cheng
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Wei, Wen
    Blekinge Institute of Technology, Faculty of Computing, Department of Technology and Aesthetics.
    Prashant, Goswami
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Guohua, Bai
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    An Improved k-Nearest Neighbours Method for Traffic Time Series Imputation2017Conference paper (Refereed)
    Abstract [en]

    Intelligent transportation systems (ITS) are becoming more and more effective, benefiting from big data. Despite this, missing data is a problem that prevents many prediction algorithms in ITS from working effectively. Much work has been done to impute those missing data. Among different imputation methods, k-nearest neighbours (kNN) has shown excellent accuracy and efficiency. However, the general kNN is designed for matrix instead of time series so it lacks the usage of time series characteristics such as windows and weights that are gap-sensitive. This work introduces gap-sensitive windowed kNN (GSW-kNN) imputation for time series. The results show that GSW-kNN is 34% more accurate than benchmarking methods, and it is still robust even if the missing ratio increases to 90%.

  • 10.
    Sun, Bin
    et al.
    Blekinge Institute of Technology, School of Computing.
    Uppatumwichian, Wipawat
    Blekinge Institute of Technology, School of Computing.
    A Study of Factors Which Influence QoD of HTTP Video Streaming Based on Adobe Flash Technology2013Independent thesis Advanced level (degree of Master (Two Years))Student thesis
    Abstract [en]

    Recently, there has been a significant rise in the Hyper-Text Transfer Protocol (HTTP) video streaming usage worldwide. However, the knowledge of performance of HTTP video streaming is still limited, especially in the aspect of factors which affect video quality. The reason is that HTTP video streaming has different characteristics from other video streaming systems. In this thesis, we show how the delivered quality of a Flash video playback is affected by different factors from diverse layers of the video delivery system, including congestion control algorithm, delay variation, playout buffer length, video bitrate and so on. We introduce Quality of Delivery Degradation (QoDD) then we use it to measure how much the Quality of Delivery (QoD) is degraded in terms of QoDD. The study is processed in a dedicated controlled environment, where we could alter the influential factors and then measure what is happening. After that, we use statistic method to analyze the data and find the relationships between influential factors and quality of video delivery which are expressed by mathematic models. The results show that the status and choices of factors have a significant impact on the QoD. By proper control of the factors, the quality of delivery could be improved. The improvements are approximately 24% by TCP memory size, 63% by congestion control algorithm, 30% by delay variation, 97% by delay when considering delay variation, 5% by loss and 92% by video bitrate.

  • 11.
    Sun, Bin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Wei, Cheng
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Liyao, Ma
    University of Jinan, CHN.
    Prashant, Goswami
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Anomaly-Aware Traffic Prediction Based on Automated Conditional Information Fusion2018In: Proceedings of 21st International Conference on Information Fusion, IEEE conference proceedings, 2018Conference paper (Refereed)
    Abstract [en]

    Reliable and accurate short-term traffic prediction plays a key role in modern intelligent transportation systems (ITS) for achieving efficient traffic management and accident detection. Previous work has investigated this topic but lacks study on automated anomaly detection and conditional information fusion for ensemble methods. This works aims to improve prediction accuracy by fusing information considering different traffic conditions in ensemble methods. In addition to conditional information fusion, a day-week decomposition (DWD) method is introduced for preprocessing before anomaly detection. A k-nearest neighbours (kNN) based ensemble method is used as an example. Real-world data are used to test the proposed method with stratified ten-fold cross validation. The results show that the proposed method with incident labels improves predictions up to 15.3% and the DWD enhanced anomaly-detection improves predictions up to 8.96%. Conditional information fusion improves ensemble prediction methods, especially for incident traffic. The proposed method works well with enhanced detections and the procedure is fully automated. The accurate predictions lead to more robust traffic control and routing systems.

  • 12.
    Sun, Bin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Wei, Cheng
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Prashant, Goswami
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Guohua, Bai
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    An Overview of Parameter and Data Strategies for K-Nearest Neighbours Based Short-Term Traffic Prediction2017In: ACM International Conference Proceeding Series Volume Part F133326, Association for Computing Machinery (ACM), 2017, p. 68-74Conference paper (Refereed)
    Abstract [en]

    Modern intelligent transportation systems (ITS) requires reliable and accurate short-term traffic prediction. One widely used method to predict traffic is k-nearest neighbours (kNN). Though many studies have tried to improve kNN with parameter strategies and data strategies, there is no comprehensive analysis of those strategies. This paper aims to analyse kNN strategies and guide future work to select the right strategy to improve prediction accuracy. Firstly, we examine the relations among three kNN parameters, which are: number of nearest neighbours (k), search step length (d) and window size (v). We also analysed predict step ahead (m) which is not a parameter but a user requirement and configuration. The analyses indicate that the relations among parameters are compound especially when traffic flow states are considered. The results show that strategy of using v leads to outstanding accuracy improvement. Later, we compare different data strategies such as flow-aware and time-aware ones together with ensemble strategies. The experiments show that the flowaware strategy performs better than the time-aware one. Thus, we suggest considering all parameter strategies simultaneously as ensemble strategies especially by including v in flow-aware strategies.

  • 13.
    Sun, Bin
    et al.
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Wei, Cheng
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Prashant, Goswami
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Guohua, Bai
    Blekinge Institute of Technology, Faculty of Computing, Department of Creative Technologies.
    Flow-Aware WPT k-Nearest Neighbours Regression for Short-Term Traffic Prediction2017In: Proceedings - IEEE Symposium on Computers and Communications, Institute of Electrical and Electronics Engineers (IEEE), 2017, Vol. 07, p. 48-53, article id 8024503Conference paper (Refereed)
    Abstract [en]

    Robust and accurate traffic prediction is critical in modern intelligent transportation systems (ITS). One widely used method for short-term traffic prediction is k-nearest neighbours (kNN). However, choosing the right parameter values for kNN is problematic. Although many studies have investigated this problem, they did not consider all parameters of kNN at the same time. This paper aims to improve kNN prediction accuracy by tuning all parameters simultaneously concerning dynamic traffic characteristics. We propose weighted parameter tuples (WPT) to calculate weighted average dynamically according to flow rate. Comprehensive experiments are conducted on one-year real-world data. The results show that flow-aware WPT kNN performs better than manually tuned kNN as well as benchmark methods such as extreme gradient boosting (XGB) and seasonal autoregressive integrated moving average (SARIMA). Thus, it is recommended to use dynamic parameters regarding traffic flow and to consider all parameters at the same time.

1 - 13 of 13
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf