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Special Issue "Machine Learning and Entropy: Discover Unknown Unknowns in Complex Data Sets"
A special issue of Entropy (ISSN 1099-4300).
Deadline for manuscript submissions: closed (30 January 2016).
In the real world, we are confronted, not only with complex and high-dimensional data sets, but also usually with noisy, incomplete, and uncertain data, where the application of traditional methods of knowledge discovery and data mining always entail the danger of modeling artifacts. Originally, information entropy was introduced by Shannon (1949), as a measure of uncertainty in data. Up to the present, many different types of entropy methods with a large number of different purposes and possible application areas have emerged. In this Special Issue we are seeking papers discussing advances in the application of learning algorithms and entropy for use in knowledge discovery and data mining, to discover unknowns in complex data sets, e.g., for biomarker discovery in biomedical data sets.
Prof. Dr. Andreas Holzinger
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. Entropy 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.
- Machine Learning
- Knowledge Discovery
- Entropy-based Data Mining