Machine Learning and Knowledge Extraction | Highly Cited Papers in 2022

Machine Learning and Knowledge Extraction (MAKE, ISSN: 2504-4990) provides an advanced forum for studies related to all areas of machine learning and knowledge extraction. It publishes reviews, regular research papers, communications, perspectives, and viewpoints, as well as Special Issues on particular subjects.

As all the articles published in our journal are in an open access format, you have free and unlimited access to the full text. We welcome you to read our most highly cited papers published in 2022 listed below:

1. “Fairness and Explanation in AI-Informed Decision Making”
by Alessa Angerschmid, Jianlong Zhou, Kevin Theuerman, Fang Chen and Andreas Holzinger
Mach. Learn. Knowl. Extr. 2022, 4(2), 556-579; https://doi.org/10.3390/make4020026
Available online: https://www.mdpi.com/2504-4990/4/2/26

2. “Machine Learning in Disaster Management: Recent Developments in Methods and Applications”
by Vasileios Linardos, Maria Drakaki, Panagiotis Tzionas and Yannis L. Karnavas
Mach. Learn. Knowl. Extr. 2022, 4(2), 446-473; https://doi.org/10.3390/make4020020
Available online: https://www.mdpi.com/2504-4990/4/2/20

3. “Machine Learning Based Restaurant Sales Forecasting”
by Austin Schmidt, Md Wasi Ul Kabir and Md Tamjidul Hoque
Mach. Learn. Knowl. Extr. 2022, 4(1), 105-130; https://doi.org/10.3390/make4010006
Available online: https://www.mdpi.com/2504-4990/4/1/6

4. “Machine and Deep Learning Applications to Mouse Dynamics for Continuous User Authentication”
by Nyle Siddiqui, Rushit Dave, Mounika Vanamala and Naeem Seliya
Mach. Learn. Knowl. Extr. 2022, 4(2), 502-518; https://doi.org/10.3390/make4020023
Available online: https://www.mdpi.com/2504-4990/4/2/23

5. “A Transfer Learning Evaluation of Deep Neural Networks for Image Classification”
by Nermeen Abou Baker, Nico Zengeler and Uwe Handmann
Mach. Learn. Knowl. Extr. 2022, 4(1), 22-41; https://doi.org/10.3390/make4010002
Available online: https://www.mdpi.com/2504-4990/4/1/2

6. “Input/Output Variables Selection in Data Envelopment Analysis: A Shannon Entropy Approach”
by Pejman Peykani, Fatemeh Sadat Seyed Esmaeili, Mirpouya Mirmozaffari, Armin Jabbarzadeh and Mohammad Khamechian
Mach. Learn. Knowl. Extr. 2022, 4(3), 688-699; https://doi.org/10.3390/make4030032
Available online: https://www.mdpi.com/2504-4990/4/3/32

7. “Actionable Explainable AI (AxAI): A Practical Example with Aggregation Functions for Adaptive Classification and Textual Explanations for Interpretable Machine Learning”
by Anna Saranti, Miroslav Hudec, Erika Mináriková, Zdenko Takáč, Udo Großschedl, Christoph Koch, Bastian Pfeifer, Alessa Angerschmid and Andreas Holzinger
Mach. Learn. Knowl. Extr. 2022, 4(4), 924-953; https://doi.org/10.3390/make4040047
Available online: https://www.mdpi.com/2504-4990/4/4/47

8. “Hierarchical Reinforcement Learning: A Survey and Open Research Challenges”
by Matthias Hutsebaut-Buysse, Kevin Mets and Steven Latré
Mach. Learn. Knowl. Extr. 2022, 4(1), 172-221; https://doi.org/10.3390/make4010009
Available online: https://www.mdpi.com/2504-4990/4/1/9

9. “Robust Reinforcement Learning: A Review of Foundations and Recent Advances”
by Janosch Moos, Kay Hansel, Hany Abdulsamad, Svenja Stark, Debora Clever and Jan Peters
Mach. Learn. Knowl. Extr. 2022, 4(1), 276-315; https://doi.org/10.3390/make4010013
Available online: http://www.mdpi.com/2504-4990/4/1/13

10. “An Attention-Based ConvLSTM Autoencoder with Dynamic Thresholding for Unsupervised Anomaly Detection in Multivariate Time Series”
by Tareq Tayeh, Sulaiman Aburakhia, Ryan Myers and Abdallah Shami
Mach. Learn. Knowl. Extr. 2022, 4(2), 350-370; https://doi.org/10.3390/make4020015
Available online: https://www.mdpi.com/2504-4990/4/2/15