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Algorithms
Special Issues
Granular Computing: From Foundations to Applications
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Granular Computing: From Foundations to Applications

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Evolutionary Algorithms and Machine Learning".

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 14789

Special Issue Editors

Dr. Antonello Rizzi

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Guest Editor
Department of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00185 Roma, RM, Italy
Interests: soft computing; pattern recognition; computational intelligence; supervised and unsupervised data driven modeling techniques; neural networks; fuzzy systems; evolutionary algorithm
Special Issues, Collections and Topics in MDPI journals
Dr. Alessio Martino

E-Mail Website
Guest Editor
Italian National Research Council, Institute of Cognitive Sciences and Technologies (ISTC-CNR), Via San Martino della Battaglia 44, 00185 Rome, Italy
Interests: machine learning; computational intelligence; big data analysis; bioinformatics; computational biology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We invite you to submit your latest research in the field of granular computing to this Special Issue called “Granular Computing: From Foundations to Applications”. Granular computing is a rapidly changing multidisciplinary information processing paradigm suitable for modeling complex systems and for extracting knowledge from data by means of suitable entities known as information granules. According to this paradigm, a given system can be observed at different levels of granularity, showing or hiding details and peculiarities of the system as a whole. Given a specific data-driven modeling problem, automatically finding a suitable resolution (semantic) level in order to gather the maximum amount of knowledge from the data at hand is a challenging task. With this Special Issue, we would like to embrace both fundamental/methodological aspects and applications related to granular computing.

We welcome high-quality research papers addressing and reviewing theoretical and practical issues of granular computing, focusing on complex systems modeling, parallel and distributed big data analysis, data granulation and its impact on knowledge discovery, problemsolving and decision-making systems, and advanced pattern recognition systems.

Similarly, we welcome research papers on cutting-edge applications, including (but not limited to) bioinformatics and computational biology, image analysis, natural language processing, sentiment and behavior analysis, time-series forecasting, and cybersecurity.

Prof. Dr. Antonello Rizzi
Dr. Alessio Martino
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Algorithms is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Granular Computing
  • Machine Learning
  • Knowledge Discovery
  • Complex Systems

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Published Papers (4 papers)

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Research

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13 pages, 381 KiB  
Article
Adaptive Quick Reduct for Feature Drift Detection
by Alessio Ferone and Antonio Maratea
Algorithms 2021, 14(2), 58; https://doi.org/10.3390/a14020058 - 11 Feb 2021
Cited by 6 | Viewed by 2205
Abstract
Data streams are ubiquitous and related to the proliferation of low-cost mobile devices, sensors, wireless networks and the Internet of Things. While it is well known that complex phenomena are not stationary and exhibit a concept drift when observed for a sufficiently long [...] Read more.
Data streams are ubiquitous and related to the proliferation of low-cost mobile devices, sensors, wireless networks and the Internet of Things. While it is well known that complex phenomena are not stationary and exhibit a concept drift when observed for a sufficiently long time, relatively few studies have addressed the related problem of feature drift. In this paper, a variation of the QuickReduct algorithm suitable to process data streams is proposed and tested: it builds an evolving reduct that dynamically selects the relevant features in the stream, removing the redundant ones and adding the newly relevant ones as soon as they become such. Tests on five publicly available datasets with an artificially injected drift have confirmed the effectiveness of the proposed method. Full article
(This article belongs to the Special Issue Granular Computing: From Foundations to Applications)
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14 pages, 423 KiB  
Article
The Auto-Diagnosis of Granulation of Information Retrieval on the Web
by Anna Bryniarska
Algorithms 2020, 13(10), 264; https://doi.org/10.3390/a13100264 - 16 Oct 2020
Cited by 2 | Viewed by 2435
Abstract
In this paper, a postulation on the relationship between the memory structure of the brain’s neural network and the representation of information granules in the semantic web is presented. In order to show this connection, abstract operations of inducing information granules are proposed [...] Read more.
In this paper, a postulation on the relationship between the memory structure of the brain’s neural network and the representation of information granules in the semantic web is presented. In order to show this connection, abstract operations of inducing information granules are proposed to be used for the proposed logical operations systems, hereinafter referred to as: analysis, reduction, deduction and synthesis. Firstly, the searched information is compared with the information represented by the thesaurus, which is equivalent to the auto-diagnosis of this system. Secondly, triangular norm systems (information perception systems) are built for fuzzy or vague information. These are fuzzy sets. The introduced logical operations and their logical values, denoted as problematic, hypothetical, validity and decidability, are interpreted in these fuzzy sets. In this way, the granularity of the information retrieval on the Web is determined according to the type of reasoning. Full article
(This article belongs to the Special Issue Granular Computing: From Foundations to Applications)
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21 pages, 1390 KiB  
Article
(Hyper)Graph Embedding and Classification via Simplicial Complexes
by Alessio Martino, Alessandro Giuliani and Antonello Rizzi
Algorithms 2019, 12(11), 223; https://doi.org/10.3390/a12110223 - 25 Oct 2019
Cited by 26 | Viewed by 5844
Abstract
This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of [...] Read more.
This paper investigates a novel graph embedding procedure based on simplicial complexes. Inherited from algebraic topology, simplicial complexes are collections of increasing-order simplices (e.g., points, lines, triangles, tetrahedrons) which can be interpreted as possibly meaningful substructures (i.e., information granules) on the top of which an embedding space can be built by means of symbolic histograms. In the embedding space, any Euclidean pattern recognition system can be used, possibly equipped with feature selection capabilities in order to select the most informative symbols. The selected symbols can be analysed by field-experts in order to extract further knowledge about the process to be modelled by the learning system, hence the proposed modelling strategy can be considered as a grey-box. The proposed embedding has been tested on thirty benchmark datasets for graph classification and, further, we propose two real-world applications, namely predicting proteins’ enzymatic function and solubility propensity starting from their 3D structure in order to give an example of the knowledge discovery phase which can be carried out starting from the proposed embedding strategy. Full article
(This article belongs to the Special Issue Granular Computing: From Foundations to Applications)
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Review

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23 pages, 504 KiB  
Review
About Granular Rough Computing—Overview of Decision System Approximation Techniques and Future Perspectives
by Piotr Artiemjew
Algorithms 2020, 13(4), 79; https://doi.org/10.3390/a13040079 - 29 Mar 2020
Cited by 4 | Viewed by 2910
Abstract
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Granular Computing: From Foundations to Applications)
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