Despite more than two decades of continuous development learning from imbalanced data is still a focus of intense research. Starting as a problem of skewed distributions of binary tasks, this topic evolved way beyond this conception. With the expansion of machine learning and data mining, combined with the arrival of big data era, we have…
Progress in Artificial Intelligence Template
Write in a clean editor, then format for Progress in Artificial Intelligence in one click — DocuGuru applies the official Springer Nature template with numbered references and exports a submission-ready PDF plus the editable LaTeX source. Free to start.
About the Progress in Artificial Intelligence format
Progress in Artificial Intelligence is a peer-reviewed journal published by Springer Nature, covering Machine Learning and Data Classification, Imbalanced Data Classification Techniques, Metaheuristic Optimization Algorithms Research.
| Publisher | Springer Nature |
|---|---|
| Reference style | Numbered (Springer) Numbered — [1], [2] in the text 1. Smith, A., Jones, B., Lee, C.: A representative article title. Progress in Artificial Intelligence 12, 45–58 (2023)
Formats any DOI in Progress in Artificial Intelligence style. No sign-up. |
| Publishes research in | Machine Learning and Data Classification Imbalanced Data Classification Techniques Metaheuristic Optimization Algorithms Research Topic Modeling Anomaly Detection Techniques and Applications |
| ISSN | 2192-6352 |
| Citation impact (2-yr) | 2.6 |
| h-index | 37 |
| i10-index | 159 |
| Total citations | 10,690 |
| Article processing charge | $2,780 |
| Top institutions publishing here | Universidad de Granada |
| Journal website | www.springer.com |
| You get | A submission-ready PDF and the editable LaTeX source — ready to submit. |
Papers published in Progress in Artificial Intelligence per year
Citation impact of Progress in Artificial Intelligence by publication year
Citations each year’s papers have accumulated so far — the most recent years are still building up.
Most-cited papers in Progress in Artificial Intelligence
Event labeling is the process of marking events in unlabeled data. Traditionally, this is done by involving one or more human experts through an expensive and time-consuming task. In this article we propose an event labeling system relying on an ensemble of detectors and background knowledge. The target data are the usage log of a…