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Competition system for automatically evaluating edge detection algorithms based on the evolutionary game theory

The Journal of China Universities of Posts and Telecommunications 2019年第5期26卷 49-59页

作者： Peng Hong Ding Guangtai Zhang Huiran Hu Dongli School of Computer Engineering and Science Shanghai UniversityShanghai 200444China Materials Genome Institute of Shanghai University Shanghai 200444China School of Materials Science and Engineering Shanghai 200444China

An ensemble learning algorithm based on game theory is proposed to evaluate algorithms of image analysis and image feature extraction.A competition system is established to implement the algorithm for evaluating the applicability and efficiency of different edge detection *** the game in the algorithm competition system,the most suitable algorithm as a winner in the competition can be selected.A group of optimal parameters for the corresponding edge detection can also be ***,based on the evolutionary game theory,a strategy of the competition of edge extraction algorithms is ***,after selecting the most suitable algorithm from the candidates,the overall parameters are *** show that for a specific class of images,several candidate algorithms can be used as a class of preference algorithms based on the final evolutionary *** analyzing the images,the priority algorithm can be recommended as the best edge detection algorithm from these reference *** is more effective than traditional methods in determining an algorithm and choosing parameters.

关键词： algorithm evaluation competition system game theory edge detection

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A MCDM-Based evaluation Approach for Imbalanced Classification Methods in Financial Risk Prediction

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IEEE ACCESS 2019年 7卷 84897-84906页

作者： Song, Yongming Peng, Yi Shandong Technol & Business Univ Sch Business Adm Yantai 264005 Peoples R China Univ Elect Sci & Technol China Sch Management & Econ Chengdu 611731 Sichuan Peoples R China

Various classifiers have been proposed for financial risk prediction. The traditional practice of using a singular performance metric for classifier evaluation is not sufficient for imbalanced classification. This paper proposes a multi-criteria decision making (MCDM)-based approach to evaluate imbalanced classifiers in credit and bankruptcy risk prediction by considering multiple performance metrics simultaneously. An experimental study is designed to provide a comprehensive evaluation of imbalanced classifiers using the proposed evaluation approach over seven financial imbalanced data sets from the UCI Machine Learning Repository. The TOPSIS, a well-known MCDM method, was applied to rank three categories of imbalanced classifiers using six popular evaluation criteria. The rankings results indicate that: 1) the rankings generated by the TOPSIS, which combine the results of six evaluation criteria, provide a more reasonable evaluation of imbalanced classifiers over any single performance criterion;and 2) Synthetic Minority Oversampling Technique (SMOTE)-based ensemble techniques outperform other groups of imbalanced learning approaches. Specifically, SMOTEBoost-C4.5, SMOTE-C4.5, and SMOTE-MLP were ranked as the top three classifiers based on their performances on the six criteria.

关键词： Financial risk prediction imbalanced classification multiple criteria decision making (MCDM) algorithm evaluation

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EnsembleLens: Ensemble-based Visual Exploration of Anomaly Detection algorithms with Multidimensional Data

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2019年第1期25卷 109-119页

作者： Xu, Ke Xia, Meng Mu, Xing Wang, Yun Cao, Nan Hong Kong Univ Sci & Technol Hong Kong Peoples R China Tongji Univ IDVx Lab Shanghai Peoples R China

The results of anomaly detection are sensitive to the choice of detection algorithms as they are specialized for different properties of data, especially for multidimensional data. Thus, it is vital to select the algorithm appropriately. To systematically select the algorithms, ensemble analysis techniques have been developed to support the assembly and comparison of heterogeneous algorithms. However, challenges remain due to the absence of the ground truth, interpretation, or evaluation of these anomaly detectors. In this paper, we present a visual analytics system named EnsembleLens that evaluates anomaly detection algorithms based on the ensemble analysis process. The system visualizes the ensemble processes and results by a set of novel visual designs and multiple coordinated contextual views to meet the requirements of correlation analysis, assessment and reasoning of anomaly detection algorithms. We also introduce an interactive analysis workflow that dynamically produces contextualized and interpretable data summaries that allow further refinements of exploration results based on user feedback. We demonstrate the effectiveness of EnsembleLens through a quantitative evaluation, three case studies with real-world data and interviews with two domain experts.

关键词： algorithm evaluation Ensemble Analysis Anomaly Detection Visual Analysis Multidimensional Data

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Solving the storm split-merge problem-A combined storm identification, tracking algorithm

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ATMOSPHERIC RESEARCH 2019年 218卷 335-346页

作者： Zan, Beilei Yu, Ye Li, Jianglin Zhao, Guo Zhang, Tong Ge, Jun Chinese Acad Sci Northwest Inst Ecoenvironm & Resources Key Lab Land Surface Proc & Climate Change Cold & 320 Donggang West Rd Lanzhou 730000 Gansu Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci Pingliang Land Surface Proc & Severe Weather Res Pingliang 744015 Gansu Peoples R China Nanjing Univ Inst Climate & Global Change Res Sch Atmospher Sci Nanjing Jiangsu Peoples R China Univ New South Wales ARC Ctr Excellence Climate Extremes Sydney NSW Australia Univ New South Wales Climate Change Res Ctr Sydney NSW Australia

Many storm identification and tracking algorithms based on radar data have been designed and widely used in weather forecasting. However, most of these algorithms have concentrated on storm cells. As a convective storm splits (merges), many storm cells will be generated (disappeared) within close proximity to each other resulting in errors in storm tracking and analysis of storm evolution. To resolve this, a new combined storm identification tracking algorithm (referred as CSIT) is devised after detailed testing of various existing convective storm identification and tracking methods. The connected neighborhoods labeling and a lower radar reflectivity threshold(30dBz) is used in CSIT, which ensures that newly formed storms can be detected. Moreover, with a lower reflectivity threshold, merger of convective cells is regarded as one convective storm, thus reducing the occurrence of storm split-merge. In terms of storm tracking, five tracking algorithms with different storm motion estimation, search radius calculation, and matching principles are designed and quantitatively evaluated using the contingency table approach and objective method. The best-performing algorithm that considers different situations of storm splitting and merging is selected to devise CSIT and the optimal thresholds for two parameters in the algorithm (i.e. maximum matching distance and search radius adjust factor) is determined through a series of sensitivity tests and objective evaluation using six years of warm season (JJA) radar data from 2012 to 2017. The devised storm identification and tracking algorithm has the potential to be applied to other data, such as cloud top brightness temperature from satellites, lightning frequency and other model output variables. The objective evaluation method does not rely on manual tracking results and thus can be used to improve and adapt automatic tracking algorithms for different situations (different storm types, regions etc).

关键词： Storm identification Storm tracking Split-merge algorithm evaluation

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evaluation of data analytics based clustering algorithms for knowledge mining in a student engagement data

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INTELLIGENT DATA ANALYSIS 2019年第5期23卷 1055-1071页

作者： Oladipupo, O. O. Olugbara, O. O. Durban Univ Technol ICT & Soc Res Grp POB 1334 ZA-4000 Durban South Africa

The application of algorithms based on data analytics for the task of knowledge mining in a student dataset is an important strategy for improving learning outcomes, student success and supporting strategic decision making in higher educational institutions of learning. However, the widely used data analytics based clustering algorithms are highly data dependent, making it pertinent to find the most effective algorithm for knowledge mining in a dataset associated with student engagement. In this study, performances of five famous clustering algorithms are evaluated for this purpose. The k-means algorithm was benchmarked with 22 distance functions based on the Silhouette index, Dunn's index and partition entropy internal validity metrics. The hierarchical clustering algorithm was benchmarked with the Cophenetic correlation coefficient computed for different combinations of distance and linkage functions. The Fuzzy c-means algorithm was benchmarked with the partition entropy, partition coefficient, Silhouette index and modified partition coefficient. The k-nearest neighbor algorithm was applied to determine the optimum epsilon value for the density-based spatial clustering of applications with noise. The default parameter settings were accepted for the expectation-maximization algorithm. The overall ranking of the clustering algorithms was based on cluster potentiality using the median deviation statistics. The results of the evaluation show the well-known k-means algorithm to have the highest cluster potentiality, demonstrating its effectiveness for the task of knowledge mining in a student engagement dataset.

关键词： algorithm evaluation data analytics data clustering knowledge mining student engagement

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Heracles: A framework for developing and evaluating text mining algorithms

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EXPERT SYSTEMS WITH APPLICATIONS 2019年 127卷 68-84页

作者： Schouten, Kim Frasincar, Flavius Dekker, Rommert Riezebos, Mark Erasmus Univ POB 1738 NL-3000 DR Rotterdam Netherlands

Many of today's businesses are driven by data, and while traditionally only quantitative data is considered, the role of textual data in our digital world is rapidly increasing. Text mining allows to extract and aggregate numerical data from textual documents, which in turn can be used to improve key decision processes. In this paper, we propose Heracles, a framework for developing and evaluating text mining algorithms, with a broad range of applications in industry. In contrast to other frameworks, Heracles supports both the development and evaluation stages of text mining algorithms. A practical use case shows the versatility and ease-of-use of the proposed framework in the domain of aspect-based sentiment analysis. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Text mining algorithm evaluation Research and development Developers framework

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Quantitative assessment of optical coherence tomography angiography algorithms for neuroimaging

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JOURNAL OF BIOPHOTONICS 2020年第9期13卷 e202000181-e202000181页

作者： Liu, Jianting Zhu, Jiang Zhu, Lianqing Yang, Qiang Fan, Fan Zhang, Fan Beijing Informat Sci & Technol Univ Minist Educ Optoelect Measurement Technol & Instr Key Lab Beijing 100192 Peoples R China Beijing Informat Sci & Technol Univ Beijing Lab Biomed Testing Technol & Instruments Beijing Peoples R China Beijing Informat Sci & Technol Univ Beijing Key Lab Optoelect Measurement Technol Beijing Peoples R China

Optical coherence tomography (OCT) angiography can noninvasively map microvascular networks and quantify blood flow in a cerebral cortex with a resolution of 1 to 10 mu m and a penetration depth of 2 to 3 mm incorporating OCT signals and angiography algorithms. Different angiography algorithms have been developed in recent years;however, the performance of the algorithms has not been assessed quantitatively for neuroimaging applications. In this paper, we developed four metrics including vascular connectivity, contrast-to-noise ratio, signal-to-noise ratio and processing time to quantitatively assess the performance of OCT angiography algorithms in image quality and computation speed. After the imaging of a rat cortex using an OCT system, the cerebral microvascular networks were visualized by seven algorithms, and the performance of the algorithms was quantified and compared. Quantitative performance assessment of the algorithms can provide suggestions for the selection of appropriate OCT angiography algorithms in neuroimaging.

关键词： algorithm evaluation angiography neuroimaging optical coherence tomography vascular networks

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Identification of evaluation criteria for algorithms used within the context of product development

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Procedia CIRP 2020年 91卷 508-515页

作者： Michael Riesener Christian Doelle Michael Mendl-Heinisch Niclas Klumpen Laboratory for Machine Tools and Production Engineering WZL at RWTH Aachen University Campus-Boulevard 30 52074 Aachen Germany

Manufacturing companies are facing multiple challenges within the product development process nowadays. A dynamic environment due to new technologies, political influences on products as well as unstable markets are raising the complexity of product development processes. To address these uncertainties, the use Artificial Intelligence is increasing. Based on the availability of large data sets and computing capacity to analyse these data, especially the field of Machine Learning algorithms, as a subsection of Artificial Intelligence, it has high potentials to increase effectiveness and efficiency within a wide variety of different fields. One of these fields, in which Machine Learning algorithms can be used, is a variety of activities within the product development process. The paper presents an evaluation method, which helps to identify specific strengths and weaknesses of selected algorithms to identify possible usage within this process. First, the work describes different algorithms, categorizes them, and selects the most relevant for further evaluation. Second, a method to evaluate different Machine Learning algorithms is developed based on a literature review on influence factors for those algorithms. The objective of the evaluation method is to give users without in-depth knowledge of Machine Learning algorithms an applicable possibility to choose a potential algorithm. Finally, the most relevant algorithms are identified based on the literature findings. Those are evaluated based on the identified criteria to simplify the selection of a Machine Learning algorithm for product development tasks.

关键词： Machine Learning Artificial Intelligence Design Product Development algorithm evaluation

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Research on Risk Assessment Method of Distribution Network Operation

International Core Journal of Engineering

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International Core Journal of Engineering 2020年第1期6卷 302-308页

作者： Juan Li Mengke Liao Jianbing Fan Qing Li Gang Liang Miao Tian Erbiao Zhou

Assessing the operational risk of the grid can effectively understand the weak links in the grid for the sake of ensuring the safe and stable operation of the grid. Based on the risk theory and combined with the particularity of the distribution network, it is necessary to analyze the operational risk and establish the calculation model of operational risk. The existing risk assessment characteristics are illustrated, and the risk assessment algorithm applicable to the distribution network is selected. The algorithm is applied to the risk index of scheduling decision taken the typical feeder distribution system as an example to prove the practicability and manipulability.

关键词： Distribution network risk algorithm evaluation example

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An evaluation Framework and algorithms for Train Rescheduling

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algorithmS 2020年第12期13卷 332页

作者： Josyula, Sai Prashanth Krasemann, Johanna Tornquist Lundberg, Lars Blekinge Inst Technol Dept Comp Sci S-37141 Karlskrona Sweden

In railway traffic systems, whenever disturbances occur, it is important to effectively reschedule trains while optimizing the goals of various stakeholders. algorithms can provide significant benefits to support the traffic controllers in train rescheduling, if well integrated into the overall traffic management process. In the railway research literature, many algorithms are proposed to tackle different versions of the train rescheduling problem. However, limited research has been performed to assess the capabilities and performance of alternative approaches, with the purpose of identifying their main strengths and weaknesses. evaluation of train rescheduling algorithms enables practitioners and decision support systems to select a suitable algorithm based on the properties of the type of disturbance scenario in focus. It also guides researchers and algorithm designers in improving the algorithms. In this paper, we (1) propose an evaluation framework for train rescheduling algorithms, (2) present two train rescheduling algorithms: a heuristic and a MILP-based exact algorithm, and (3) conduct an experiment to compare the two multi-objective algorithms using the proposed framework (a proof-of-concept). It is found that the heuristic algorithm is suitable for solving simpler disturbance scenarios since it is quick in producing decent solutions. For complex disturbances wherein multiple trains experience a primary delay due to an infrastructure failure, the exact algorithm is found to be more appropriate.

关键词： algorithm evaluation decision support systems parallel algorithms multi-objective optimization train rescheduling

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