2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI)最新文献

Granular representation schemes of time series: A study in an optimal allocation of information granularity 时间序列的粒度表示方案:信息粒度最优分配的研究

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602454

R. Al-Hmouz, W. Pedrycz, A. Balamash, A. Morfeq

{"title":"Granular representation schemes of time series: A study in an optimal allocation of information granularity","authors":"R. Al-Hmouz, W. Pedrycz, A. Balamash, A. Morfeq","doi":"10.1109/FOCI.2013.6602454","DOIUrl":"https://doi.org/10.1109/FOCI.2013.6602454","url":null,"abstract":"Information granularity augments a variety of schemes of representation of time series, helps quantify the quality of models of the series and supports a thorough analysis of their parameters. This study introduces a concept of a granular representation of time series. We show that information granules formed on a basis of a given original numeric representation of the series can be optimized through a process of allocation (distribution) of information granularity being regarded here as an essential design asset. We formulate an optimization criterion and utilize a Particle Swarm Optimization (PSO) as an optimization vehicle to distribute a predefined level of information granularity. An optimization criterion used in the formation of the granular representation scheme is concerned with expressing and maximizing coverage of available temporal data by their granular representation. Experimental results in which we focus on the Piecewise Aggregate Approximation (PAA) offer details of the optimization of the allocation of granularity completed for some synthetic and real-world time series and quantify the performance of the resulting granular schemes of representation of time series.","PeriodicalId":237129,"journal":{"name":"2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"22 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":"114694517","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

On the measure of incoherent information in extended multi-adjoint logic programs 扩展多伴随逻辑程序中非相干信息的测度

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602452

N. Madrid, M. Ojeda‐Aciego

引用次数: 7

Linguistic summaries for periodicity detection based on mathematical morphology 基于数学形态学的周期性检测语言摘要

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602462

Gilles Moyse, Marie-Jeanne Lesot, B. Bouchon-Meunier

引用次数: 27

The X-mu approach: Fuzzy quantities, fuzzy arithmetic and fuzzy association rules X-mu方法:模糊量、模糊算法和模糊关联规则

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602451

T. Martin, B. Azvine

引用次数: 21

A ripple-spreading algorithm for route optimization 一种路由优化的波纹扩散算法

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602455

Xiao-Bing Hu, Ming Wang, Qiong Sun, M. Leeson, E. Paolo

引用次数: 5

Foundations of relational similarity-based query language RESIQL 基于关系相似性的查询语言RESIQL的基础

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602450

Petr Krajča, Vilém Vychodil

{"title":"Foundations of relational similarity-based query language RESIQL","authors":"Petr Krajča, Vilém Vychodil","doi":"10.1109/FOCI.2013.6602450","DOIUrl":"https://doi.org/10.1109/FOCI.2013.6602450","url":null,"abstract":"We present a query language for similarity-based relational model of data with support of ordinal ranking and imperfect matches. The model utilized in this paper results from Codd's model of data by considering general residuated partially ordered scales of degrees of satisfaction in place of the two-element Boolean scale implicitly used in the Codd model. As a result, we look at the traditional relational model of data from the perspective of general scales allowing us to represent partial matches of relational queries. From the data representation point of view, the basic structures representing both the stored data and results of queries are so-called ranked data tables, generalizing the ordinary data tables (relations). Compared to other approaches dealing with ranking and imperfect matches, ours has several benefits: (i) it is based on solid logical foundations-we do not introduce an additional ranking module on top of the Codd model but have all the necessary concepts as an integral part of the model, (ii) the model is very general and several ranking procedures introduced in the past can be shown to be particular cases of our model, (iii) the model is not oriented just to query answering, it allows to handle similarity-based data dependencies, approximate constraints and other issues that cannot be properly formalized in the classic Codd model, (iv) despite of its generality, query execution in the model is tractable. In this paper, we present logical foundations of the query language and show how it implements the underlying similarity-based model of data.","PeriodicalId":237129,"journal":{"name":"2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"139 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":"126719286","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

Incorporating user constraints into topic-oriented self-organizing maps 将用户约束合并到面向主题的自组织映射中

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602460

Hsin-Chang Yang, Chung-Hong Lee, Chun-Yen Wu

引用次数: 4

Quantifying accuracy of learning via sample width 通过样本宽度量化学习的准确性

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602459

M. Anthony, Joel Ratsaby

引用次数: 0

Computing non-redundant sets of functional dependencies via simplification 通过简化计算功能依赖的非冗余集

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602449

P. Cordero, M. Enciso, Á. Mora, J. M. Rodríguez-Jiménez

引用次数: 2

Effect of model complexity for estimation of distribution algorithm in NK landscapes 模型复杂度对NK景观分布算法估计的影响

2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI) Pub Date : 2013-04-16 DOI: 10.1109/FOCI.2013.6602458

Rung-Tzuo Liaw, Chuan-Kang Ting

{"title":"Effect of model complexity for estimation of distribution algorithm in NK landscapes","authors":"Rung-Tzuo Liaw, Chuan-Kang Ting","doi":"10.1109/FOCI.2013.6602458","DOIUrl":"https://doi.org/10.1109/FOCI.2013.6602458","url":null,"abstract":"Evolutionary algorithms (EAs) have been widely proved to be effective in solving complex problems. Estimation of distribution algorithm (EDA) is an emerging EA, which manipulates probability models instead of genes for evolution EDA creates probability models based on the promising solution in the population and generates offspring by sampling from these models. The model complexity is a key factor in the performance of EDA. Complex models can express the relations among variables more accurately than simple models. However, for some problems with strong interaction among variables, building a model for all the relations becomes unrealistic and impractical due to its high computational cost and requirement for a large population size. This study aims to understand the behaviors of EDAs with different model complexities in NK landscapes. Specifically, this study compares the solution quality and convergence speed of univariate marginal distribution algorithm (UMDA), bivariate marginal distribution algorithm (BMDA), and estimation of Bayesian network (EBNA) in the NK landscapes with different parameter settings. The comparative results reveal that high complexity does not imply high performance: Simple model such as UMDA and BMDA can outperform complex mode like EBNA on the tested NK landscape problems. The results also show that BMDA achieves a stable high probability of generating the best solution and satisfactory solution quality; by contrast, the probability for EBNA drastically declines after some generations.","PeriodicalId":237129,"journal":{"name":"2013 IEEE Symposium on Foundations of Computational Intelligence (FOCI)","volume":"9 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":"122396610","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: 7