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2010 22nd IEEE International Conference on Tools with Artificial Intelligence最新文献

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Adaptive and Dynamic Service Composition Using Q-Learning 基于q -学习的自适应动态服务组合
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.28
Hongbing Wang, Xuan Zhou, Xiaoping Zhou, Weihong Liu, Wenya Li
{"title":"Adaptive and Dynamic Service Composition Using Q-Learning","authors":"Hongbing Wang, Xuan Zhou, Xiaoping Zhou, Weihong Liu, Wenya Li","doi":"10.1109/ICTAI.2010.28","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.28","url":null,"abstract":"In a dynamic environment, some services may become unavailable, some new services may be published and the various properties of the services, such as their prices and performance, may change. Thus, to ensure user satisfaction in the long run, it is desirable that a service composition can automatically adapt to these changes. To this end, we leverage the technology of reinforcement learning and propose a mechanism for adaptive service composition. The mechanism requires no prior knowledge about services’ quality, while being able to achieve the optimal composition solution. In addition, it allows a composite service to dynamically adjust itself to fit a varying environment. We present the design of our mechanism, and demonstrate its effectiveness through an extensive experimental evaluation.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127540142","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: 22
Active Learning for Semi-Supervised K-Means Clustering 半监督k均值聚类的主动学习
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.11
V. Vu, Nicolas Labroche, B. Bouchon-Meunier
{"title":"Active Learning for Semi-Supervised K-Means Clustering","authors":"V. Vu, Nicolas Labroche, B. Bouchon-Meunier","doi":"10.1109/ICTAI.2010.11","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.11","url":null,"abstract":"K-Means algorithm is one of the most used clustering algorithm for Knowledge Discovery in Data Mining. Seed based K-Means is the integration of a small set of labeled data (called seeds) to the K-Means algorithm to improve its performances and overcome its sensitivity to initial centers. These centers are, most of the time, generated at random or they are assumed to be available for each cluster. This paper introduces a new efficient algorithm for active seeds selection which relies on a Min-Max approach that favors the coverage of the whole dataset. Experiments conducted on artificial and real datasets show that, using our active seeds selection algorithm, each cluster contains at least one seed after a very small number of queries and thus helps reducing the number of iterations until convergence which is crucial in many KDD applications.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"33 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126855864","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: 25
GreenSim: A Network Simulator for Comprehensively Validating and Evaluating New Machine Learning Techniques for Network Structural Inference GreenSim:用于全面验证和评估网络结构推理的新机器学习技术的网络模拟器
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.105
C. Fogelberg, V. Palade
{"title":"GreenSim: A Network Simulator for Comprehensively Validating and Evaluating New Machine Learning Techniques for Network Structural Inference","authors":"C. Fogelberg, V. Palade","doi":"10.1109/ICTAI.2010.105","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.105","url":null,"abstract":"Networks are very important in many fields of machine learning research. Within networks research, inferring the structure of unknown networks is often a key problem; e.g. of genetic regulatory networks. However, there are very few well-known biological networks, and good simulation is essential for validating and evaluating novel structural inference techniques. Further, the importance of large, genome-wide structural inference is increasingly recognised, but there does not appear to be a good simulator available for large networks. This paper presents GreenSim, a simulator that helps address this gap. GreenSim automatically generates large, genome-size networks with more biologically realistic structural characteristics and 2nd-order non-linear regulatory functions. The simulator itself and the novel method used for generating a network structure with appropriate in- and out-degree distributions may also generalise easily to other types of network. GreenSim is available online at: http://syntilect.com/cgf/pubs:software","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"255 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114447886","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: 3
An Effective Multilevel Memetic Algorithm for Balanced Graph Partitioning 平衡图划分的一种有效的多级模因算法
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.25
Una Benlic, Jin-Kao Hao
{"title":"An Effective Multilevel Memetic Algorithm for Balanced Graph Partitioning","authors":"Una Benlic, Jin-Kao Hao","doi":"10.1109/ICTAI.2010.25","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.25","url":null,"abstract":"The balanced graph partitioning consists in dividing the vertices of an undirected graph into a given number of subsets of approximately equal size, such that the number of edges crossing the subsets is minimized. In this work, we present a multilevel memetic algorithm for this NP-hard problem that relies on a powerful grouping recombination operator and a dedicated local search procedure. The proposed operator tends to preserve the backbone with respect to a set of parent individuals, i.e. the grouping of vertices which is same throughout each parent individual. Although our approach requires significantly longer computing time compared to some current state-of-art graph partitioning algorithms such as SCOTCH, METIS, CHACO, JOSTLE, etc., it competes very favorably with these approaches in terms of solution quality. Moreover, it easily reaches or improves on the best partitions ever reported in the literature.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124635221","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: 13
Preferred Explanations for Quantified Constraint Satisfaction Problems 量化约束满足问题的首选解释
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.47
D. Mehta, B. O’Sullivan, L. Quesada
{"title":"Preferred Explanations for Quantified Constraint Satisfaction Problems","authors":"D. Mehta, B. O’Sullivan, L. Quesada","doi":"10.1109/ICTAI.2010.47","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.47","url":null,"abstract":"The Quantified Constraint Satisfaction Problem(QCSP) is a generalization of the classical constraint satisfaction problem in which some variables can be universally quantified. This additional expressiveness can help model problems in which a subset of the variables take value assignments that are outside the control of the decision maker. Typical examples of such domains are game-playing, conformant planning and reasoning under uncertainty. In these domains decision makers need explanations when a QCSP does not admit a winning strategy. We present an approach to defining preferences amongst the requirements of a QCSP, and an approach to finding most preferred explanations of inconsistency based on preferences over relaxations of quantifiers and constraints. This paper unifies work from the fields of constraint satisfaction, explanation generation, and reasoning under preferences and uncertainty.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123958629","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
An Adaptive Fuzzy Classifier Approach to Edge Detection in Latent Fingerprint Images 一种基于模糊自适应分类器的潜在指纹图像边缘检测方法
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.32
J. Rochac, L. Liang, Byunggu Yu, Zhao Lu
{"title":"An Adaptive Fuzzy Classifier Approach to Edge Detection in Latent Fingerprint Images","authors":"J. Rochac, L. Liang, Byunggu Yu, Zhao Lu","doi":"10.1109/ICTAI.2010.32","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.32","url":null,"abstract":"This paper proposes an Adaptive Fuzzy Classifier Approach (AFCA) to local edge detection in order to address the challenges of detecting latent fingerprint in severely degraded images. The proposed approach adapts classifier parameters to different parts of input images using the concept of reference neighborhood. Three variants of AFCAs, namely K-means-clustering AFCA, Entropy-based AFCA, and Statistical AFCA, were developed. Experiments were conducted both on synthetic images and on real fingerprint images to compare these AFCAs and Canny edge detection. The presented results show that Statistical AFCA is the best performer with latent images.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114370766","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: 3
A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm 基于k近邻的多实例多标签学习算法
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.102
Min-Ling Zhang
{"title":"A k-Nearest Neighbor Based Multi-Instance Multi-Label Learning Algorithm","authors":"Min-Ling Zhang","doi":"10.1109/ICTAI.2010.102","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.102","url":null,"abstract":"In multi-instance multi-label learning (i.e. MIML), each example is not only represented by multiple instances but also associated with multiple labels. Most existing algorithms solve MIML problem via the intuitive way of identifying its equivalence in degenerated version of MIML. However, this identification process may lose useful information encoded in training examples and therefore be harmful to the learning algorithm's performance. In this paper, a novel algorithm named MIML-kNN is proposed for MIML by utilizing the popular k-nearest neighbor techniques. Given a test example, MIML-kNN not only considers its neighbors, but also considers its citers which regard it as their own neighbors. The label set of the test example is determined by exploiting the labeling information conveyed by its neighbors and citers. Experiments on two real-world MIML tasks, i.e. scene classification and text categorization, show that MIML-kNN achieves superior performance than some existing MIML algorithms.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114617075","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: 69
Using Multiagent Self-organization Techniques to Improve Dynamic Skill Searching in Virtual Social Communities 基于多智能体自组织技术的虚拟社区动态技能搜索
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.74
Annabelle Mercier, M. Occello, Jean-Paul Jamont
{"title":"Using Multiagent Self-organization Techniques to Improve Dynamic Skill Searching in Virtual Social Communities","authors":"Annabelle Mercier, M. Occello, Jean-Paul Jamont","doi":"10.1109/ICTAI.2010.74","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.74","url":null,"abstract":"This paper introduces an approach which aims at improving information access (skills, services) in large networks. A self-organized multiagent analysis of the problem, reducing the number of messages transmitted for a skill search is proposed. The MWAC (Multi-Wireless-Agent Communication) model is extended here to take some specificities of social networks into account, like the information held by the social network members and their connections with the others.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114863755","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
Enhancing Clause Learning by Symmetry in SAT Solvers 利用SAT求解器的对称性增强小句学习
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.55
B. Benhamou, T. Nabhani, R. Ostrowski, M. Saïdi
{"title":"Enhancing Clause Learning by Symmetry in SAT Solvers","authors":"B. Benhamou, T. Nabhani, R. Ostrowski, M. Saïdi","doi":"10.1109/ICTAI.2010.55","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.55","url":null,"abstract":"The satisfiability problem (SAT) is shown to be the first decision NP-complete problem. It is central in complexity theory. A CNF formula usually contains an interesting number of symmetries. That is, the formula remains invariant under some variable permutations. Such permutations are the symmetries of the formula, their elimination can lead to make a short proof for a satisfiability proof procedure. On other hand, many improvements had been done in SAT solving, Con???ict-Driven Clause Learning (CDCL) SAT solvers are now able to solve great size and industrial SAT instances efficiently. The main theoretical key behind these modern solvers is, they use lazy data structures, a restart policy and perform clause learning at each fail end point in the search tree. Although symmetry and clause learning are shown to be powerful principles for SAT solving, but their combination, as far as we now, is not investigated. In this paper, we will show how symmetry can be used to improve clause learning in CDCL SAT solvers. We implemented the symmetry clause learning approach on the MiniSat solver and experimented it on several SAT instances. We compared both MiniSat with and without symmetry and the results obtained are very promising and show that clause learning by symmetry is profitable for CDCL SAT solvers.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124227957","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: 22
Instance-Based Ensemble Pruning via Multi-Label Classification 基于实例的多标签分类集成剪枝
2010 22nd IEEE International Conference on Tools with Artificial Intelligence Pub Date : 2010-10-27 DOI: 10.1109/ICTAI.2010.64
Fotini Markatopoulou, Grigorios Tsoumakas, I. Vlahavas
{"title":"Instance-Based Ensemble Pruning via Multi-Label Classification","authors":"Fotini Markatopoulou, Grigorios Tsoumakas, I. Vlahavas","doi":"10.1109/ICTAI.2010.64","DOIUrl":"https://doi.org/10.1109/ICTAI.2010.64","url":null,"abstract":"Ensemble pruning is concerned with the reduction of the size of an ensemble prior to its combination. Its purpose is to reduce the space and time complexity of the ensemble and/or to increase the ensemble's accuracy. This paper focuses on instance-based approaches to ensemble pruning, where a different subset of the ensemble may be used for each different unclassified instance. We propose modeling this task as a multi-label learning problem, in order to take advantage of the recent advances in this area for the construction of effective ensemble pruning approaches. Results comparing the proposed framework against a variety of other instance-based ensemble pruning approaches in a variety of datasets using a heterogeneous ensemble of 200 classifiers, show that it leads to improved accuracy.","PeriodicalId":141778,"journal":{"name":"2010 22nd IEEE International Conference on Tools with Artificial Intelligence","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2010-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126467187","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: 17
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