About Machine Learning and Knowledge Extraction
Machine Learning and Knowledge Extraction (MAKE) is an inter-disciplinary, cross-domain, peer-reviewed, scholarly open access journal to provide a platform to support the international machine learning community. It publishes original research articles, reviews, tutorials, research ideas, short notes and Special Issues that focus on machine learning and applications. Papers which deal with fundamental research questions to help reach a level of useable computational intelligence are very welcome. Please see our video on YouTube explaining our journal MAKE concept.
The unique features of this journal:
- Promotion of a cross-disciplinary approach addressing seven sections (see scope) to concert international efforts without boundaries, supporting collaborative and transdisciplinary research between experts from these seven fields.
- Appraisal of different fields will foster diverse perspectives and opinions, hence offering a platform for novel ideas and a fresh look on methodologies to put original ideas into effect for the benefit of all.
- Stimulation of replications and further research by inclusion of data and/or software offering the full details of experimental work as supplementary material or by providing links to repositories (e.g. Github)
MAKE is a new online journal in the field of Machine Learning and Knowledge Extraction. It will not compete with the likes of other existing excellent journals, e.g. JMLR, Springer MACH, KAIS, etc. rather it is complementary to these. The aim for MAKE is to support the international research community, for example to elucidate on new approaches (e.g., statistical machine learning) compared to classic and traditional ones (e.g., ontological approaches), which will be a further great step in promoting the field and to bring solutions to diverse application domains.
This journal fosters an integrated machine learning approach, i.e. it supports the whole machine learning and knowledge extraction and discovery pipeline from data pre-processing to visualization of the results, always with a strong focus on privacy, data protection, safety and security aspects. Topics are categorized in seven sections to support the integrative machine learning approach:
- Data: data ecosystems, data-preprocessing, data integration, data fusion, data mapping, data generation, and knowledge representation
- Learning: automatic and interactive machine learning methodologies, methods, algorithms and tools, comparisons to human cognition
- Visualization: Data visualization, visual analysis, comparisons to human perception, human-computer interaction
- Privacy: data protection, safety and security, interpretability, transparency, causality, usability, acceptance, ethical, legal and social issues
- Network: Graph-based machine learning, graph data mining, language graphs, probabilistic graphical models
- Topology: Topological data analysis, computational topology, homology, homotopy, persistence, manifolds, simplical complexes
- Entropy: Entropy-based data mining, longitudinal and time dependent data analysis and knowledge discovery
see the inaugural editorial paper for details at: https://www.mdpi.com/2504-4990/1/1/1.
MDPI Publication Ethics Statement
MAKE is a member of the Committee on Publication Ethics (COPE). MDPI takes the responsibility to enforce a rigorous peer-review together with strict ethical policies and standards to ensure to add high quality scientific works to the field of scholarly publication. Unfortunately, cases of plagiarism, data falsification, inappropriate authorship credit, and the like, do arise. MDPI takes such publishing ethics issues very seriously and our editors are trained to proceed in such cases with a zero tolerance policy. To verify the originality of content submitted to our journals, we use iThenticate to check submissions against previous publications. MDPI works with Publons to provide reviewers with credit for their work.
Authors and publishers are encouraged to send review copies of their recent related books to the following address. Received books will be listed as Books Received within the journal's News & Announcements section.
Copyright / Open Access
Articles published in MAKE will be Open-Access articles distributed under the terms and conditions of the Creative Commons Attribution License (CC BY). The copyright is retained by the author(s). MDPI will insert the following note at the end of the published text:
Announcement and Advertisement
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