2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)最新文献_第2页

Improving genetic programming classification for binary and multiclass datasets 改进二值和多类数据集的遗传规划分类

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597232

Nailah Al-Madi, Simone A. Ludwig

引用次数: 11

Software cost estimation by classical and Fuzzy Analogy for Web Hypermedia Applications: A replicated study 基于经典和模糊类比的Web超媒体应用软件成本估算:一项重复研究

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597238

A. Idri, Azeddine Zahi

引用次数: 19

A semantic subspace learning method to exploit relevance feedback log data for image retrieval 利用相关反馈日志数据进行图像检索的语义子空间学习方法

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597234

Lining Zhang, Lipo Wang, Weisi Lin

{"title":"A semantic subspace learning method to exploit relevance feedback log data for image retrieval","authors":"Lining Zhang, Lipo Wang, Weisi Lin","doi":"10.1109/CIDM.2013.6597234","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597234","url":null,"abstract":"Conventional content-based image retrieval (CBIR) systems with the Euclidean distance metric in a high-dimensional visual feature space usually cannot achieve satisfactory performance due to the semantic gap. Relevance feedback (RF) has been introduced as a powerful tool to involve the user in the system to improve the performance of CBIR. Despite the success, an on-line learning task can be tedious and boring for the user. Various schemes have been proposed to exploit the RF log data to further enhance the performance of CBIR. In this paper, we propose a semantic subspace learning (SSL) method to exploit the RF log data with contextual information for an image retrieval task. Different from conventional subspace learning approaches, our method can directly learn a semantic concept subspace from the RF log data with contextual information without using any class label information. We show that the performance of the image retrieval task can be significantly improved in the low-dimensional semantic concept subspace. Extensive experiments on a real-world image database demonstrate the effectiveness of the proposed scheme in improving the performance of CBIR by exploiting the RF log data.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115116013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 9

Business models enhancement through discovery of roles 通过发现角色来增强业务模型

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597224

Andrea Burattin, A. Sperduti, Marco Veluscek

{"title":"Business models enhancement through discovery of roles","authors":"Andrea Burattin, A. Sperduti, Marco Veluscek","doi":"10.1109/CIDM.2013.6597224","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597224","url":null,"abstract":"Control flow discovery algorithms are able to reconstruct the workflow of a business process from a log of performed activities. These algorithms, however, do not pay attention to the reconstruction of roles, i.e. they do not group activities according to the skills required to perform them. Information about roles in business processes is commonly considered important and explicitly integrated into the process representation, e.g. as swimlanes in BPMN diagrams. This work proposes an approach to enhance a business process model with information on roles. Specifically, the identification of roles is based on the detection of handover of roles. On the basis of candidates for roles handover, the set of activities is first partitioned and then subsets of activities which are performed by the same originators are merged, so to obtain roles. All significant partitions of activities are automatically generated. Experimental results on several logs show that the set of generated roles is not too large and it always contains the correct definition of roles. We also propose an entropy based measure to rank the candidate roles which returns promising experimental results.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128555975","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 38

Selection of a robust experimental design for the effective modeling of nonlinear systems using Genetic Programming 采用遗传规划对非线性系统进行有效建模的鲁棒实验设计选择

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597249

A. Garg, K. Tai

{"title":"Selection of a robust experimental design for the effective modeling of nonlinear systems using Genetic Programming","authors":"A. Garg, K. Tai","doi":"10.1109/CIDM.2013.6597249","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597249","url":null,"abstract":"The evolutionary approach of Genetic Programming (GP) has been applied extensively to model various non-linear systems. The distinct advantage of using GP is that prior assumptions for the selection of a model structure are not required. The GP automatically evolves the optimal model structure and its parameters that best describe the system characteristics. However, the evolution of an optimal model structure is highly dependent on the experimental designs used to sample the problem (system) domain and capture its characteristics. The literature reveals that very few researchers have studied the effect of various experimental designs on the performance of GP models and therefore the optimum choice of an experimental design is still unknown. This paper studies the effect of various experimental designs on the performance of GP models on two non-linear test functions. The objective of the paper is to identify the most robust (best) experimental design for effective modeling of non-linear test functions using GP. The analysis reveals that for the test function 1, the experimental design that gives best performance of GP models is response surface faced design and for test function 2, the best experimental design is 5-level full factorial design. Thus, the result concludes that the selection of the robust experimental design is a crucial preprocessing step for the effective modeling of non-linear systems using GP.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"99 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133417150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 33

Nonnegative matrix factorization and artificial immune based classification for fault diagnosis of diesel valve train 基于非负矩阵分解和人工免疫分类的柴油机配气机构故障诊断

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597245

Yongsheng Yang, Gang Li, Yongsheng Zhu, Youyun Zhang

引用次数: 3

A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM 一个全面的基准测试框架(CoBeFra)，用于过程过程模型和ProM中的事件日志之间的一致性分析

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597244

S. V. Broucke, Jochen De Weerdt, J. Vanthienen, B. Baesens

{"title":"A comprehensive benchmarking framework (CoBeFra) for conformance analysis between procedural process models and event logs in ProM","authors":"S. V. Broucke, Jochen De Weerdt, J. Vanthienen, B. Baesens","doi":"10.1109/CIDM.2013.6597244","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597244","url":null,"abstract":"Process mining encompasses the research area which is concerned with knowledge discovery from information system event logs. Within the process mining research area, two prominent tasks can be discerned. First of all, process discovery deals with the automatic construction of a process model out of an event log. Secondly, conformance checking focuses on the assessment of the quality of a discovered or designed process model in respect to the actual behavior as captured in event logs. Hereto, multiple techniques and metrics have been developed and described in the literature. However, the process mining domain still lacks a comprehensive framework for assessing the goodness of a process model from a quantitative perspective. In this study, we describe the architecture of an extensible framework within ProM, allowing for the consistent, comparative and repeatable calculation of conformance metrics. For the development and assessment of both process discovery as well as conformance techniques, such a framework is considered greatly valuable.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129267806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 46

A novel quantum cooperative co-evolutionary algorithm for large-scale minimum attribute reduction optimization 大规模最小属性约简优化的量子协同进化算法

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597248

Weiping Ding, Quan Shi, Senbo Chen, Zhijing Guan, Jiandong Wang

{"title":"A novel quantum cooperative co-evolutionary algorithm for large-scale minimum attribute reduction optimization","authors":"Weiping Ding, Quan Shi, Senbo Chen, Zhijing Guan, Jiandong Wang","doi":"10.1109/CIDM.2013.6597248","DOIUrl":"https://doi.org/10.1109/CIDM.2013.6597248","url":null,"abstract":"Due to the fact that conventional evolution-based attribute reduction algorithms are poor efficiency in accomplishing large-scale attribute reduction, a novel and efficient quantum cooperative co-evolutionary algorithm (named QCCAR) for minimum attribute reduction optimization in large-scale datasets is proposed in this paper. First, the self-adaptive quantum rotation angle and quantum entanglement strategies are adopted to update the operation of quantum revolving door, and the population diversity and convergence to the global optimum ensure to be improved fast. Second, a local-global best performance based cooperative co-evolutionary paradigm is designed to divide large-scale attribute sets into reasonable subsets, which are adaptively produced based on the assignment of decomposer credit and probability. Third, the representative of the subpopulation is selected to evolve the corresponding decomposed attribute subset so that the global optimization reduction set can be obtained quickly. The experimental results demonstrate that the proposed algorithm has better feasibility and effectiveness, comparison with other state-of-the-art algorithms. So it can provide an efficient solution to finding minimum attribute reduction for large-scale datasets.","PeriodicalId":220189,"journal":{"name":"2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124332628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Kernel spectral clustering for predicting maintenance of industrial machines 核谱聚类预测工业机械维修

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597215

R. Langone, C. Alzate, B. Ketelaere, J. Suykens

引用次数: 12

Combining Semi-Supervised and active learning for hyperspectral image classification 结合半监督和主动学习的高光谱图像分类

2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM) Pub Date : 2013-04-16 DOI: 10.1109/CIDM.2013.6597222

Mingzhi Li, Rui Wang, K. Tang

引用次数: 12