Int. J. Rough Sets Data Anal.最新文献_第10页

FLANN + BHO: A Novel Approach for Handling Nonlinearity in System Identification FLANN + BHO:一种处理系统辨识非线性的新方法

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/IJRSDA.2018010102

B. Naik, Janmenjoy Nayak, H. Behera

引用次数: 5

Two Rough Set-based Software Tools for Analyzing Non-Deterministic Data 两种基于粗糙集的非确定性数据分析软件工具

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/ijrsda.2014010103

Mao Wu, M. Nakata, H. Sakai

引用次数: 30

Forecasting Exchange Rates: A Chaos-Based Regression Approach 汇率预测:一种基于混沌的回归方法

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/ijrsda.2015010103

A-K. A. Radhwan, Mahmoud Kamel, M. Dahab, A. Hassanien

{"title":"Forecasting Exchange Rates: A Chaos-Based Regression Approach","authors":"A-K. A. Radhwan, Mahmoud Kamel, M. Dahab, A. Hassanien","doi":"10.4018/ijrsda.2015010103","DOIUrl":"https://doi.org/10.4018/ijrsda.2015010103","url":null,"abstract":"Accurate forecasting for future events constitutes a fascinating challenge for theoretical and for applied researches. Foreign Exchange market FOREX is selected in this research to represent an example of financial systems with a complex behavior. Forecasting a financial time series can be a very hard task due to the inherent uncertainty nature of these systems. It seems very difficult to tell whether a series is stochastic or deterministic chaotic or some combination of these states. More generally, the extent to which a non-linear deterministic process retains its properties when corrupted by noise is also unclear. The noise can affect a system in different ways even though the equations of the system remain deterministic. Since a single reliable statistical test for chaoticity is not available, combining multiple tests is a crucial aspect, especially when one is dealing with limited and noisy data sets like in economic and financial time series. In this research, the authors propose an improved model for forecasting exchange rates based on chaos theory that involves phase space reconstruction from the observed time series and the use of support vector regression SVR for forecasting.Given the exchange rates of a currency pair as scalar observations, observed time series is first analyzed to verify the existence of underlying nonlinear dynamics governing its evolution over time. Then, the time series is embedded into a higher dimensional phase space using embedding parameters.In the selection process to find the optimal embedding parameters,a novel method based on the Differential Evolution DE geneticalgorithmas a global optimization technique was applied. The authors have compared forecasting accuracy of the proposed model against the ordinary use of support vector regression. The experimental results demonstrate that the proposed method, which is based on chaos theory and genetic algorithm,is comparable with the existing approaches.","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121223311","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: 40

Hybrid TRS-FA Clustering Approach for Web2.0 Social Tagging System Web2.0社会标签系统的混合TRS-FA聚类方法

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/ijrsda.2015010105

H. Inbarani, S. S. Kumar

引用次数: 24

A Fuzzy Knowledge Based Fault Tolerance Mechanism for Wireless Sensor Networks 基于模糊知识的无线传感器网络容错机制

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/IJRSDA.2018010107

S. Acharya, C. Tripathy

引用次数: 24

Probability Based Most Informative Gene Selection From Microarray Data 基于概率的微阵列数据最信息基因选择

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/IJRSDA.2018010101

Sunanda Das, A. Das

{"title":"Probability Based Most Informative Gene Selection From Microarray Data","authors":"Sunanda Das, A. Das","doi":"10.4018/IJRSDA.2018010101","DOIUrl":"https://doi.org/10.4018/IJRSDA.2018010101","url":null,"abstract":"Microarraydatasetshaveawideapplication inbioinformatics research.Analysis tomeasure the expressionlevelofthousandsofgenesofthiskindofhigh-throughputdatacanhelpforfindingthe causeandsubsequenttreatmentofanydisease.Therearemanytechniquesingeneanalysistoextract biologicallyrelevantinformationfrominconsistentandambiguousdata.Inthispaper,theconceptsof functionaldependencyandclosureofanattributeofdatabasetechnologyareusedforfindingthemost importantsetofgenesforcancerdetection.Firstly,themethodcomputessimilarityfactorbetween eachpairofgenes.Basedonthesimilarityfactorsasetofgenedependencyisformedfromwhich closuresetisobtained.Subsequently,conditionalprobabilitybasedinterestingnessmeasurementsare usedtodeterminethemostinformativegenefordiseaseclassification.Theproposedmethodisapplied onsomepubliclyavailablecancerousgeneexpressiondataset.Theresultshowstheeffectiveness androbustnessofthealgorithm. KeywoRDS Important Gene Set, Most Informative Gene Selection, Probability Factor, Similarity Based Gene Dependency","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125820034","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: 26

Illness Narrative Complexity in Right and Left-Hemisphere Lesions 左右半球病变的疾病叙事复杂性

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/IJRSDA.2016010103

U. Giani, Carmine Garzillo, Brankica Pavic, Margaret Piscitelli

引用次数: 5

Information Systems on Hesitant Fuzzy Sets 关于犹豫模糊集的信息系统

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/IJRSDA.2016010105

D. Deepak, S. J. John

引用次数: 24

A Study on Bayesian Decision Theoretic Rough Set 贝叶斯决策理论粗糙集的研究

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/ijrsda.2014010101

S. Halder

{"title":"A Study on Bayesian Decision Theoretic Rough Set","authors":"S. Halder","doi":"10.4018/ijrsda.2014010101","DOIUrl":"https://doi.org/10.4018/ijrsda.2014010101","url":null,"abstract":"The concept of rough set was first developed by Pawlak (1982). After that it has been successfully applied in many research fields, such as pattern recognition, machine learning, knowledge acquisition, economic forecasting and data mining. But the original rough set model cannot effectively deal with data sets which have noisy data and latent useful knowledge in the boundary region may not be fully captured. In order to overcome such limitations, some extended rough set models have been put forward which combine with other available soft computing technologies. Many researchers were motivated to investigate probabilistic approaches to rough set theory. Variable precision rough set model (VPRSM) is one of the most important extensions. Bayesian rough set model (BRSM) (Slezak & Ziarko, 2002), as the hybrid development between rough set theory and Bayesian reasoning, can deal with many practical problems which could not be effectively handled by original rough set model. Based on Bayesian decision procedure with minimum risk, Yao (1990) puts forward a new model called decision theoretic rough set model (DTRSM) which brings new insights into the probabilistic approaches to rough set theory. Throughout this paper, the concept of decision theoretic rough set is studied and also a new concept of Bayesian decision theoretic rough set is introduced. Lastly a comparative study is done between Bayesian decision theoretic rough set and Rough set defined by Pawlak (1982).","PeriodicalId":152357,"journal":{"name":"Int. J. Rough Sets Data Anal.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122112143","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: 30

Performance Analysis of Hard and Soft Clustering Approaches For Gene Expression Data 基因表达数据的软硬聚类性能分析

Int. J. Rough Sets Data Anal. Pub Date : 1900-01-01 DOI: 10.4018/ijrsda.2015010104

P. K. N. Banu, S. Andrews

引用次数: 33