Evaluating the Elite: A Guide to the Top Journals in Machine Learning and their Impact Metrics
Research Thinking - Publish or Perish
Impact factor and CORE (Conference on Research and Technology in Europe) ranking are two ways to measure the quality and impact of scientific journals. The impact factor is a measure of the frequency with which the average article in a journal has been cited in a given year. The CORE ranking is a measure of the overall quality of a journal based on a number of factors, including its impact factor, the number of articles published, and the number of citations received.
Here are some of the top journals in machine learning, along with their impact factors and CORE rankings:
Journal of Machine Learning Research (JMLR): Impact factor: NA, CORE ranking: A*
Transactions on Neural Networks and Learning Systems (TNNLS): Impact factor: 7.8, CORE ranking: A*
Machine Learning Journal: Impact factor: 5.7, CORE ranking: A
Artificial Intelligence Journal: Impact factor: 4.0, CORE ranking: A*
Pattern Recognition Journal: Impact factor: 4.5, CORE ranking: A*
Transactions on Pattern Analysis and Machine Intelligence (TPAMI): Impact factor: 8.0, CORE ranking: A*
Please note that these impact factors and CORE rankings are subject to change and may vary from year to year. It's also worth mentioning that impact factor and CORE ranking are just two of many factors to consider when evaluating the quality and impact of a journal, and they should not be used as the sole criteria for evaluating the quality of a journal or paper.