Special Issue "Supervised and Unsupervised Classification Algorithms"

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

Deadline for manuscript submissions: 31 July 2020.

Special Issue Editors

Dr. Mario Rosario Guarracino
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Guest Editor
The Institute for High Performance Computing and Networking (ICAR-CNR), Napoli,Italy; University of Cassino and Southern Lazio, Italy
Interests: data science; statistical network analysis; supervised classification
Dr. Laura Antonelli
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Guest Editor
Computational Science and Data Group, The Institute for High Performance Computing and Networking (ICAR-CNR), Napoli, Italy
Interests: scientific computing; high-performance computing; image processing
Special Issues and Collections in MDPI journals
Dr. Pietro Hiram Guzzi
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Guest Editor
School of Computer Science and Biomedical Engineering, University "Magna Græcia" of Catanzaro, 88100 Catanzaro CZ, Itália
Interests: network biology; graph analytics; bioinformatic
Special Issues and Collections in MDPI journals

Special Issue Information

Dear Colleagues,

Supervised and unsupervised classification algorithms are the two main branches of machine learning methods. Supervised classification refers to the task of training a system using labeled data divided into classes, and assigning data to these existing classes. The process consists in computing a model from a set of labeled training data, and then applying the model to predict the class label for incoming unlabeled data. It is called supervised learning because the training data set supervises the learning process. Supervised classification algorithms are divided into two categories: classification and regression.

In unsupervised classification, the data being processed are unlabeled, so in the lack of prior knowledge, the algorithm tries to search for a similarity to generate clusters and assign classes. Unsupervised classification algorithms are divided into three categories: clustering, data estimation, and dimensionality reduction.

Applications range from object detection from biomedical images and disease prediction to natural language understanding and generation.

Submissions are welcome both for traditional classification problems as well as new applications. Potential topics include but are not limited to image classification, data integration, clustering approaches, feature extraction, etc.

Dr. Mario Rosario Guarracino
Dr. Laura Antonelli
Dr. Pietro Hiram Guzzi
Guest Editors

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 papers will be 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 1000 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

  • Supervised classification
  • Clustering
  • Network analysis
  • Community extraction
  • Data science
  • Biological knowledge extraction

Published Papers (2 papers)

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Research

Open AccessArticle
Fuzzy C-Means Clustering Algorithm with Multiple Fuzzification Coefficients
Algorithms 2020, 13(7), 158; https://doi.org/10.3390/a13070158 - 30 Jun 2020
Abstract
Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have [...] Read more.
Clustering is an unsupervised machine learning technique with many practical applications that has gathered extensive research interest. Aside from deterministic or probabilistic techniques, fuzzy C-means clustering (FCM) is also a common clustering technique. Since the advent of the FCM method, many improvements have been made to increase clustering efficiency. These improvements focus on adjusting the membership representation of elements in the clusters, or on fuzzifying and defuzzifying techniques, as well as the distance function between elements. This study proposes a novel fuzzy clustering algorithm using multiple different fuzzification coefficients depending on the characteristics of each data sample. The proposed fuzzy clustering method has similar calculation steps to FCM with some modifications. The formulas are derived to ensure convergence. The main contribution of this approach is the utilization of multiple fuzzification coefficients as opposed to only one coefficient in the original FCM algorithm. The new algorithm is then evaluated with experiments on several common datasets and the results show that the proposed algorithm is more efficient compared to the original FCM as well as other clustering methods. Full article
(This article belongs to the Special Issue Supervised and Unsupervised Classification Algorithms)
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Open AccessArticle
Time Series Clustering Model based on DTW for Classifying Car Parks
Algorithms 2020, 13(3), 57; https://doi.org/10.3390/a13030057 - 02 Mar 2020
Abstract
An increasing number of automobiles have led to a serious shortage of parking spaces and a serious imbalance of parking supply and demand. The best way to solve these problems is to achieve the reasonable planning and classify management of car parks, guide [...] Read more.
An increasing number of automobiles have led to a serious shortage of parking spaces and a serious imbalance of parking supply and demand. The best way to solve these problems is to achieve the reasonable planning and classify management of car parks, guide the intelligent parking, and then promote its marketization and industrialization. Therefore, we aim to adopt clustering method to classify car parks. Owing to the time series characteristics of car park data, a time series clustering framework, including preprocessing, distance measurement, clustering and evaluation, is first developed for classifying car parks. Then, in view of the randomness of existing clustering models, a new time series clustering model based on dynamic time warping (DTW) is proposed, which contains distance radius calculation, obtaining density of the neighbor area, k centers initialization, and clustering. Finally, some UCR datasets and data of 27 car parks are employed to evaluate the performance of the models and results show that the proposed model performs obviously better results than those clustering models based on Euclidean distance (ED) and traditional clustering models based on DTW. Full article
(This article belongs to the Special Issue Supervised and Unsupervised Classification Algorithms)
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