Special Issue "Machine Learning on Scientific Data and Information"

A special issue of Information (ISSN 2078-2489). This special issue belongs to the section "Information Applications".

Deadline for manuscript submissions: 30 June 2019

Special Issue Editor

Guest Editor
Dr. Ka-Chun Wong

Departament of Computer Science, City University of Hong Kong, Hong Kong
Website | E-Mail
Interests: bioinformatics; computational biology; applied machine learning; data science; evolutionary computation and numerical optimization

Special Issue Information

Dear Colleagues,

In recent years, we have witnessed the explosive growth of high-throughput scientific data in different disciplines, such as bioinformatics and computational biology. Nonetheless, traditional algorithms can suffer from data scalability, noises, and curse of dimensionality. To address these issues together, new scalable machine learning algorithms have to be developed.

Therefore, we have initiated such a Special Issue in the hope that researchers will work together to alleviate and transform these challenges into opportunities for scientific advancement by proposing different kinds of machine learning algorithms.

Dr. Ka-Chun Wong
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 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. Information 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

  • Machine Learning
  • Data Science
  • Bioinformatics
  • Computational Biology

Published Papers (2 papers)

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Research

Open AccessArticle Visual Analysis Scenarios for Understanding Evolutionary Computational Techniques’ Behavior
Information 2019, 10(3), 88; https://doi.org/10.3390/info10030088
Received: 26 December 2018 / Revised: 20 February 2019 / Accepted: 20 February 2019 / Published: 28 February 2019
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Abstract
Machine learning algorithms are used in many applications nowadays. Sometimes, we need to describe how the decision models created output, and this may not be an easy task. Information visualization (InfoVis) techniques (e.g., TreeMap, parallel coordinates, etc.) can be used for creating scenarios [...] Read more.
Machine learning algorithms are used in many applications nowadays. Sometimes, we need to describe how the decision models created output, and this may not be an easy task. Information visualization (InfoVis) techniques (e.g., TreeMap, parallel coordinates, etc.) can be used for creating scenarios that visually describe the behavior of those models. Thus, InfoVis scenarios were used to analyze the evolutionary process of a tool named AutoClustering, which generates density-based clustering algorithms automatically for a given dataset using the EDA (estimation-of-distribution algorithm) evolutionary technique. Some scenarios were about fitness and population evolution (clustering algorithms) over time, algorithm parameters, the occurrence of the individual, and others. The analysis of those scenarios could lead to the development of better parameters for the AutoClustering tool and algorithms and thus have a direct impact on the processing time and quality of the generated algorithms. Full article
(This article belongs to the Special Issue Machine Learning on Scientific Data and Information)
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Open AccessArticle An Effective Feature Segmentation Algorithm for a Hyper-Spectral Facial Image
Information 2018, 9(10), 261; https://doi.org/10.3390/info9100261
Received: 22 September 2018 / Revised: 15 October 2018 / Accepted: 16 October 2018 / Published: 22 October 2018
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Abstract
The human face as a biometric trait has been widely used for personal identity verification but it is still a challenging task under uncontrolled conditions. With the development of hyper-spectral imaging acquisition technology, spectral properties with sufficient discriminative information bring new opportunities for [...] Read more.
The human face as a biometric trait has been widely used for personal identity verification but it is still a challenging task under uncontrolled conditions. With the development of hyper-spectral imaging acquisition technology, spectral properties with sufficient discriminative information bring new opportunities for a facial image process. This paper presents a novel ensemble method for skin feature segmentation of a hyper-spectral facial image based on a k-means algorithm and a spanning forest algorithm, which exploit both spectral and spatial discriminative features. According to the closed skin area, local features are selected for further facial image analysis. We present the experimental results of the proposed algorithm on various public face databases which achieve higher segmentation rates. Full article
(This article belongs to the Special Issue Machine Learning on Scientific Data and Information)
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