Bayesian analysis of time series using granular computing approach

Hryniewicz, Olgierd; Kaczmarek, Katarzyna

doi:10.1016/j.asoc.2014.11.024

Cited by 29 publications

(15 citation statements)

References 58 publications

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“…In the recent paper by Kacprzyk et al [14], the authors claim that linguistic data summaries in Yager's sense can be considered an ultimately human consistent form of human-centric aggregation. This paper extends our previous works devoted to the statistical process control that aimed at the monitoring of the autocorrelated health-related processes [10,11,17] and works dedicated to the incorporation of linguistic summaries into time series forecasting [16].…”

Section: Related Worksupporting

confidence: 57%

Personalized linguistic summaries in smartphone-based monitoring of bipolar disorder patients

Kaczmarek-Majer¹,

Hryniewicz²,

Dominiak³

et al. 2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

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Personalized linguistic summaries are developed with the use of protoforms in the sense of Yager and Kacprzyk. We discuss the construction and usefulness of such patientdependent and disease-state-dependent linguistic summaries that may be examplied as Most outgoing calls in mania state (disease period) are short compared to the calls recorded in the euthymia state (healthy period). Such linguistic summaries may become important features in the smartphonebased monitoring of the bipolar disorder patients and inform about the detected change in patient's state. The main advantage of the personalized linguistic summaries is their human-centricity. The performance of the proposed approach is illustrated with examples based on the real-life data collected within the observational study.

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Section: Related Worksupporting

confidence: 57%

Personalized linguistic summaries in smartphone-based monitoring of bipolar disorder patients

Kaczmarek-Majer¹,

Hryniewicz²,

Dominiak³

et al. 2019

Proceedings of the 2019 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and

Self Cite

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“…Being in the context of rough mereology was proposed by L. Polkowski and A.Skowron, approximation spaces by A. Skowron and J. Stepaniuk [8,9], and logic for approximate reasoning by L.Polkowski and M. Semeniuk-Polkowska [10], and Qing Liu [11]. Examples of interesting studies from recent years can be found in [12][13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation

Artiemjew

Ropiak

2020

Computers

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This paper is a continuation of works based on a previously developed new granulation method-homogeneous granulation. The most important new feature of this method compared to our previous ones is that there is no need to estimate optimal parameters. Approximation parameters are selected dynamically depending on the degree of homogeneity of decision classes. This makes the method fast and simple, which is an undoubted advantage despite the fact that it gives a slightly lower level of approximation to our other techniques. In this particular article, we are presenting its performance in the process of missing values absorption. We test selected strategies on synthetically damaged data from the UCI repository. The added value is to investigate the specific performance of our new granulation technique in absorbing missing values. The effectiveness of their absorption in the granulation process has been confirmed in our experiments.

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“…Of course, many other authors are conducting considerations on groups of similar objects, which is simply the most natural way of modeling problems; it is impossible to name them all. Let us quote a few very interesting works on various research topics from recent years on granular computing [13][14][15][16][17][18]. Additionally, interesting research on the field of granular computation with the use of neural network techniques can be found in the works [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

About Granular Rough Computing—Overview of Decision System Approximation Techniques and Future Perspectives

Artiemjew

2020

Algorithms

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Granular computing techniques are a huge discipline in which the basic component is to operate on groups of similar objects according to a fixed similarity measure. The first references to the granular computing can be seen in the works of Zadeh in fuzzy set theory. Granular computing allows for a very natural modelling of the world. It is very likely that the human brain, while solving problems, performs granular calculations on data collected from the senses. The researchers of this paradigm have proven the unlimited possibilities of granular computing. Among other things, they are used in the processes of classification, regression, missing values handling, for feature selection, and as mechanisms of data approximation. It is impossible to quote all methods based on granular computing—we can only discuss a selected group of techniques. In the article, we have presented a review of recently developed granulation techniques belonging to the family of approximation algorithms founded by Polkowski—in the framework of rough set theory. Starting from the basic Polkowski’s standard granulation, we have described further developed by us concept dependent, layered, and epsilon variants, and our recent homogeneous granulation. We are presenting simple numerical examples and samples of research results. The effectiveness of these methods in terms of decision system size reduction and maintenance of the internal knowledge from the original data are presented. The reduction in the number of objects in our techniques while maintaining classification efficiency reaches 90 percent—for standard granulation with usage of a kNN classifier (we achieve similar efficiency for the concept-dependent technique for the Naive Bayes classifier). The largest reduction achieved in the number of exhaustive set of rules at the efficiency level to the original data are 99 percent—it is for concept-dependent granulation. In homogeneous variants, the reduction is less than 60 percent, but the advantage of these techniques is that it is not necessary to look for optimal granulation parameters, which are selected dynamically. We also describe potential directions of development of granular computing techniques by prism of described methods.

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Bayesian analysis of time series using granular computing approach

Cited by 29 publications

References 58 publications

Personalized linguistic summaries in smartphone-based monitoring of bipolar disorder patients

Personalized linguistic summaries in smartphone-based monitoring of bipolar disorder patients

On Granular Rough Computing: Handling Missing Values by Means of Homogeneous Granulation

About Granular Rough Computing—Overview of Decision System Approximation Techniques and Future Perspectives

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