Machine Learning and knowledge Extraction

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Machine Learning and Knowledge Extraction (MAKE)  
DisciplineComputer Science & Mathematics
Edited byAndreas Holzinger
Publication details
Publication history
MDPI (Basel, Switzerland)
Open access
Standard abbreviations
Mach. Learn. Knowl. Extr.
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Machine Learning and Knowledge Extraction (MAKE) is an international, peer-reviewed, open access journal, which is published by MDPI. It aims to provide a platform to support the whole machine learning and knowledge extraction community. Though there are many existing excellent journals in this field, like Journal of Machine Learning Research and Machine Learning (Springer), MAKE doesn't complete with them, rather a complementary to these journals. MAKE has a lot of excellent colleagues from all over the world.[1]. The Editor-in-Chief is Assoc. Prof. Andreas Holzinger (Graz University of Technology, Austria).

Though MAKE is an open access journal, for well-prepared manuscripts submitted in 2018 and 2019, there is no publishing fee. Papers which deal with the following seven topics are very welcome: Data, Learning, Visualization, Privacy, Network, Topology and Entropy. For detailed explanation, you can check the inaugural paper[2]


  1. "Machine Learning and Knowledge Extraction". Retrieved 2018-05-23.
  2. Holzinger, Andreas (2017-07-03). "Introduction to MAchine Learning & Knowledge Extraction (MAKE)". Machine Learning and Knowledge Extraction. 1 (1): 1–20. doi:10.3390/make1010001.

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