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 find below a youtube video which explains our journal MAKE concept: https://www.youtube.com/watch?v=mCayuEf-akQ
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: http://www.mdpi.com/2504-4990/1/1/1.
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