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2012 IEEE 24th International Conference on Tools with Artificial Intelligence最新文献

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Data Complexity Measures and Nearest Neighbor Classifiers: A Practical Analysis for Meta-learning 数据复杂性度量和最近邻分类器:元学习的实用分析
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.150
George D. C. Cavalcanti, Ing Ren Tsang, Breno A. Vale
{"title":"Data Complexity Measures and Nearest Neighbor Classifiers: A Practical Analysis for Meta-learning","authors":"George D. C. Cavalcanti, Ing Ren Tsang, Breno A. Vale","doi":"10.1109/ICTAI.2012.150","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.150","url":null,"abstract":"The classifier accuracy is affected by the properties of the data sets used to train it. Nearest neighbor classifiers are known for being simple and accurate in several domains, but their behavior is strongly dependent on data complexity. On the other hand, there are data complexity measures which aim to describe properties of the data sets. This work aims to show how data complexity measures can be efficiently used to predict the behavior of the Nearest Neighbor classifier. Seven data complexity measures and seventeen real datasets are used in the experimental study. Each data complexity measure is analyzed individually in order to find a relationship between its value and the accuracy of the classifier on a given dataset. No single measure used is good enough to predict the behavior of the Nearest Neighbor classifier. However, the combination of these measures provides a powerful tool to predict the accuracy of the Nearest Neighbor classifier.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115500992","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: 15
Data Mining Based Collaborative Analysis of Microarray Data 基于数据挖掘的微阵列数据协同分析
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.97
G. Tsiliki, S. Kossida, Natalja Friesen, S. Rüping, M. Tzagarakis, N. Karacapilidis
{"title":"Data Mining Based Collaborative Analysis of Microarray Data","authors":"G. Tsiliki, S. Kossida, Natalja Friesen, S. Rüping, M. Tzagarakis, N. Karacapilidis","doi":"10.1109/ICTAI.2012.97","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.97","url":null,"abstract":"Biomedical research has recently seen a vast growth in publicly and instantly available information, which are often complementary or overlapping. As the available resources become more specialized, there is a growing need for multidisciplinary collaborations between biomedical researchers to address complex research questions. We present an application of a data-mining algorithm to gene-expression data in a collaborative decision-making support environment, as a typical example of how multidisciplinary researchers can collaborate in analyzing and biologically interpreting gene-expression micro array data. Through the proposed approach, researchers can easily decide about which data repositories should be considered, analyze the algorithmic results, discuss the weaknesses of the patterns identified, and set up new iterations of the data mining algorithm by defining other descriptive attributes or integrating other relevant data.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"131 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115775452","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
A Dimensionality Reduction Approach for Modular Neural Networks 模块化神经网络的一种降维方法
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.166
E. Verissimo, Diogo da Silva Severo, George D. C. Cavalcanti, Ing Ren Tsang
{"title":"A Dimensionality Reduction Approach for Modular Neural Networks","authors":"E. Verissimo, Diogo da Silva Severo, George D. C. Cavalcanti, Ing Ren Tsang","doi":"10.1109/ICTAI.2012.166","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.166","url":null,"abstract":"A modular neural network architecture is composed by independent neural networks that focus on different parts of the whole task. This work proposes the Intrinsic Modular Neural Networks that aims not only to reduce the number of classes and patterns in each independent neural network, but also to reduce the dimensionality of the data. The task decomposition is performed by the High-Dimensional Data Clustering algorithm. After the clustering, the training patterns are divided in groups and each group is used to train an independent neural network. Experiments on public databases show promising results.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125816299","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
CPrefMiner: An Algorithm for Mining User Contextual Preferences Based on Bayesian Networks 基于贝叶斯网络的用户上下文偏好挖掘算法
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.24
S. D. Amo, Marcos L. P. Bueno, Guilherme Alves, Nádia Félix F. da Silva
{"title":"CPrefMiner: An Algorithm for Mining User Contextual Preferences Based on Bayesian Networks","authors":"S. D. Amo, Marcos L. P. Bueno, Guilherme Alves, Nádia Félix F. da Silva","doi":"10.1109/ICTAI.2012.24","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.24","url":null,"abstract":"In this article we propose CPrefMiner, a mining technique for learning a Bayesian Preference Network (BPN) from a given sample of user choices. In our approach, user preferences are not static and may vary according to a multitude of user contexts. So, we name them Contextual Preferences. Contextual Preferences can be naturally expressed by a BPN. The method has been evaluated in a series of experiments executed on synthetic and real-world datasets and proved to be efficient to discover user contextual preferences.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"53 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125923177","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: 11
A Highly Efficient and Secure Multidimensional Blocking Approach for Private Record Linkage 一种高效安全的私有记录联动多维阻塞方法
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.65
Alexandros Karakasidis, Vassilios S. Verykios
{"title":"A Highly Efficient and Secure Multidimensional Blocking Approach for Private Record Linkage","authors":"Alexandros Karakasidis, Vassilios S. Verykios","doi":"10.1109/ICTAI.2012.65","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.65","url":null,"abstract":"Privacy Preserving Record Linkage is the process of securely integrating information without compromising the privacy of the individuals described by these data. While such an effort sounds appealing for both academic and business applications, it is complicated and computationally intensive. In this paper we aspire to provide a solution to this problem by presenting a highly secure mutidimensional Privacy Preserving Blocking approach which is totally distributed and runs independently on each data holder, making it invulnerable to third party attacks. It is based on the idea of using publicly available corpora of data known as reference sets for creating k-anonymous clusters. We analytically prove that our method is secure and provide experimental results which evaluate the increased performance of our method in terms of matching accuracy and execution time.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125347387","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: 4
Extended Partial-Order Dynamic Backtracking Algorithm for Dynamically Changed Environments 动态变化环境下的扩展偏序动态回溯算法
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.83
Yosra Acodad, Imade Benelallam, Saida Hammoujan, E. Bouyakhf
{"title":"Extended Partial-Order Dynamic Backtracking Algorithm for Dynamically Changed Environments","authors":"Yosra Acodad, Imade Benelallam, Saida Hammoujan, E. Bouyakhf","doi":"10.1109/ICTAI.2012.83","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.83","url":null,"abstract":"Unlike constructive approaches in which a partial assignment to the variables is incrementally extended, repair approaches start with an inconsistent assignment (e.g. old solution) and search through the space of possible repairs. In this paper, we propose an Extended Partial-order Dynamic Backtracking (EPDB) algorithm for dynamically changed environments. The EPDB allows dynamic CSPs to be dealt efficiently according to based-repair heuristic approaches. A past solution can be repaired using retroactive data structures: safety conditions and no goods, saved previously in Partial-order Dynamic Backtracking process. We evaluate our algorithm on synthetic and real problems, and experimental results show that the proposed algorithm outperforms the original algorithm PDB, in terms of run-time and constraints checks.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114904575","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
Re-configuring Legacy Instances of the Partner Units Problem 重新配置合作伙伴单元问题的遗留实例
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.29
E. Teppan
{"title":"Re-configuring Legacy Instances of the Partner Units Problem","authors":"E. Teppan","doi":"10.1109/ICTAI.2012.29","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.29","url":null,"abstract":"The Partner Units Problem (PUP) is a computationally challenging configuration problem with diverse application domains, such as railway safety or electrical engineering. The recently formulated Quick Pup algorithm has made it possible for the first time to automatically derive solutions for real world-sized problems, such that the Partner Units Configuration Problem can be seen as practically solved. Further challenges, which were clearly out of reach before, can be tackled now. This paper addresses two practically relevant problems related to the PUP. The first problem is to calculate solutions for legacy instances of the PUP. The difference to the original PUP is that the solver is given a partial solution which has to be extended to a complete solution. The second problem is about adapting partial legacy solutions which cannot be extended to complete solutions and thus making a consistent complete solution possible. All presented approaches are evaluated on behalf of a newly defined set of benchmark instances.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114454998","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 Value Ordering Heuristic for Solving Ultra-Weak Solutions in Minimax Weighted CSPs 求解极大极小加权csp超弱解的值排序启发式
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.12
Jimmy Ho-man Lee, Terrence W.K. Mak
{"title":"A Value Ordering Heuristic for Solving Ultra-Weak Solutions in Minimax Weighted CSPs","authors":"Jimmy Ho-man Lee, Terrence W.K. Mak","doi":"10.1109/ICTAI.2012.12","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.12","url":null,"abstract":"Minimax Weighted Constraint Satisfaction Problems (formerly called Quantified Weighted CSPs) are a framework for modeling soft constrained problems with adversarial conditions. In this paper, we study the effects of a value ordering heuristic in solving ultra-weak solutions on top of the alpha beta tree search with constraint propagation. The value ordering heuristic is based on minimax heuristics from adversarial search, which selects values for variables according to the semantic of quantifiers by considering the problem as a two-player zero sum game. In practice, implementing the heuristic requires costs approximations, and we devise three heuristic variants: HUnary, HBinary, and HFullBinary to approximate costs. In particular, we observe that combining these heuristic variants with consistency notions can achieve a better efficiency and a further reduction of search space. We perform experiments on three benchmarks to compare the effects on applying these heuristic variants, and confirm the feasibility and efficiency of our proposal.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"471 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122069524","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: 2
An Unsupervised Segmentation Method for Retinal Vessel Using Combined Filters 一种基于组合滤波器的视网膜血管无监督分割方法
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.106
W. S. Oliveira, Ing Ren Tsang, George D. C. Cavalcanti
{"title":"An Unsupervised Segmentation Method for Retinal Vessel Using Combined Filters","authors":"W. S. Oliveira, Ing Ren Tsang, George D. C. Cavalcanti","doi":"10.1109/ICTAI.2012.106","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.106","url":null,"abstract":"Image segmentation of retinal blood vessels is an important procedure for the prediction and diagnosis of cardiovascular related diseases, such as hypertension and diabetes, which are known to affect the retinal blood vessels appearance. This work develops an unsupervised segmentation procedure for the segmentation of retinal vessels images using a combined matched filter, Frangi filter and Gabor Wavelet Filter. After the vessel enhancement, two segmentation methods are tested. The first method uses an approach based on deformable models and the second uses fuzzy C-means for the image segmentation. The procedure is evaluated using two public image databases, Drive and Stare. The results are compared to other state-of-the-art methods described in the literature.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"72 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123193705","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
Author Identification in Imbalanced Sets of Source Code Samples 不平衡源代码样本集中的作者识别
2012 IEEE 24th International Conference on Tools with Artificial Intelligence Pub Date : 2012-11-07 DOI: 10.1109/ICTAI.2012.112
E. Chatzicharalampous, Georgia Frantzeskou, E. Stamatatos
{"title":"Author Identification in Imbalanced Sets of Source Code Samples","authors":"E. Chatzicharalampous, Georgia Frantzeskou, E. Stamatatos","doi":"10.1109/ICTAI.2012.112","DOIUrl":"https://doi.org/10.1109/ICTAI.2012.112","url":null,"abstract":"Similarly to natural language texts, source code documents can be distinguished by their style. Source code author identification can be viewed as a text classification task given that samples of known authorship by a set of candidate authors are available. Although very promising results have been reported for this task, the evaluation of existing approaches avoids focusing on the class imbalance problem and its effect on the performance. In this paper, we present a systematic experimental study of author identification in skewed training sets where the training samples are unequally distributed over the candidate authors. Two representative author identification methods are examined, one follows the profile-based paradigm (where a single representation is produced for all the available training samples per author) and the other follows the instance-based paradigm (where each training sample has its own individual representation). We examine the effect of the source code representation on the performance of these methods and show that the profile-based method is better able to handle cases of highly skewed training sets while the instance-based method is a better choice in balanced or slightly-skewed training sets.","PeriodicalId":155588,"journal":{"name":"2012 IEEE 24th International Conference on Tools with Artificial Intelligence","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2012-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116508236","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: 8
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