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Electronics
Special Issues
Fuzzy Systems and Data Mining
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Fuzzy Systems and Data Mining

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 October 2020) | Viewed by 10027

Special Issue Editor

Prof. Dr. Ferdinando Di Martino

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Guest Editor
Dipartimento di Architettura, Università degli Studi di Napoli Federico II, Via Toledo 402, 80134 Napoli, Italy
Interests: fuzzy sets and fuzzy relations; soft computing; fuzzy transform image processing theory; machine learning; data mining
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As part of the soft computing methods applied in many data mining processes, such as data clustering, classification, forecasting analysis association rules detection, and dependency analysis, fuzzy systems play a key role. This role has recently increased, with the need in various disciplines to manage imprecise, massive, and heterogeneous data. In addition, fuzzy systems can be applied in big data mining, in which textual non-structured information, such as social data documents or image and video data must be used for retrieval purposes.

The aim of this Issue is to encourage researchers and scholars to submit original contributions concerning new approaches involving fuzzy systems, for the exploration of data in complex systems, with special reference to the novel data mining methods applied to treat massive, complex, and heterogeneous information sources.

Prof. Dr. Ferdinando Di 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. Electronics is an international peer-reviewed open access semimonthly 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 2400 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

  • data mining
  • fuzzy systems
  • fuzzy clustering
  • fuzzy text mining
  • neuro-fuzzy systems
  • learning fuzzy rule base systems
  • fuzzy forecasting methods

Published Papers (3 papers)

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Research

11 pages, 531 KiB  
Article
A Novel Fuzzy Entropy-Based Method to Improve the Performance of the Fuzzy C-Means Algorithm
by Barbara Cardone and Ferdinando Di Martino
Electronics 2020, 9(4), 554; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics9040554 - 26 Mar 2020
Cited by 18 | Viewed by 2840
Abstract
One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. [...] Read more.
One of the main drawbacks of the well-known Fuzzy C-means clustering algorithm (FCM) is the random initialization of the centers of the clusters as it can significantly affect the performance of the algorithm, thus not guaranteeing an optimal solution and increasing execution times. In this paper we propose a variation of FCM in which the initial optimal cluster centers are obtained by implementing a weighted FCM algorithm in which the weights are assigned by calculating a Shannon Fuzzy Entropy function. The results of the comparison tests applied on various classification datasets of the UCI Machine Learning Repository show that our algorithm improved in all cases relating to the performances of FCM. Full article
(This article belongs to the Special Issue Fuzzy Systems and Data Mining)
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12 pages, 865 KiB  
Article
A Modified Adaptive Neuro-Fuzzy Inference System Using Multi-Verse Optimizer Algorithm for Oil Consumption Forecasting
by Mohammed A. A. Al-qaness, Mohamed Abd Elaziz, Ahmed A. Ewees and Xiaohui Cui
Electronics 2019, 8(10), 1071; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics8101071 - 21 Sep 2019
Cited by 29 | Viewed by 3641
Abstract
Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using [...] Read more.
Oil is the primary source of energy, therefore, oil consumption forecasting is essential for the necessary economic and social plans. This paper presents an alternative time series prediction method for oil consumption based on a modified Adaptive Neuro-Fuzzy Inference System (ANFIS) model using the Multi-verse Optimizer algorithm (MVO). MVO is applied to find the optimal parameters of the ANFIS. Then, the hybrid method, namely MVO-ANFIS, is employed to forecast oil consumption. To evaluate the performance of the MVO-ANFIS model, a dataset of two different countries was used and compared with several forecasting models. The evaluation results show the superiority of the MVO-ANFIS model over other models. Moreover, the proposed method constitutes an accurate tool that effectively improved the solution of time series prediction problems. Full article
(This article belongs to the Special Issue Fuzzy Systems and Data Mining)
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17 pages, 1018 KiB  
Article
EEkNN: k-Nearest Neighbor Classifier with an Evidential Editing Procedure for Training Samples
by Lianmeng Jiao, Xiaojiao Geng and Quan Pan
Electronics 2019, 8(5), 592; https://0-doi-org.brum.beds.ac.uk/10.3390/electronics8050592 - 27 May 2019
Cited by 2 | Viewed by 2720
Abstract
The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms applied in many fields because it is very simple to understand and easy to design. However, one of the major problems encountered in using the kNN [...] Read more.
The k-nearest neighbor (kNN) rule is one of the most popular classification algorithms applied in many fields because it is very simple to understand and easy to design. However, one of the major problems encountered in using the kNN rule is that all of the training samples are considered equally important in the assignment of the class label to the query pattern. In this paper, an evidential editing version of the kNN rule is developed within the framework of belief function theory. The proposal is composed of two procedures. An evidential editing procedure is first proposed to reassign the original training samples with new labels represented by an evidential membership structure, which provides a general representation model regarding the class membership of the training samples. After editing, a classification procedure specifically designed for evidently edited training samples is developed in the belief function framework to handle the more general situation in which the edited training samples are assigned dependent evidential labels. Three synthetic datasets and six real datasets collected from various fields were used to evaluate the performance of the proposed method. The reported results show that the proposal achieves better performance than other considered kNN-based methods, especially for datasets with high imprecision ratios. Full article
(This article belongs to the Special Issue Fuzzy Systems and Data Mining)
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