Machine Learning

Aims & Scope

Machine Learning is an international forum for research on computational approaches to learning.

The journal publishes articles reporting substantive results on a wide range of learning methods applied to a variety of learning problems, including but not limited to: Learning Problems: Classification, regression, recognition, and prediction; Problem solving and planning; Reasoning and inference; Data mining; Web mining; Scientific discovery; Information retrieval; Natural language processing; Design and diagnosis; Vision and speech perception; Robotics and control; Combinatorial optimization; Game playing; Industrial, financial, and scientific applications of all kinds.

Learning Methods: Supervised and unsupervised learning methods (including learning decision and regression trees, rules, connectionist networks, probabilistic networks and other statistical models, inductive logic programming, case-based methods, ensemble methods, clustering, etc.); Reinforcement learning; Evolution-based methods; Explanation-based learning; Analogical learning methods; Automated knowledge acquisition; Learning from instruction; Visualization of patterns in data; Learning in integrated architectures; Multistrategy learning; Multi-agent learning.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	2.9
2024	4.30

Journal Rank

Year	Value
2024	3849

Journal Citation Indicator

Year	Value
2024	2565

SJR (SCImago Journal Rank)

Year	Value
2024	1.147

Quartile

Year	Value
2024	Q1

h-index

Year	Value
2024	175

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Computer Science, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Random Forests

  Citation: 98879

  Authors: Leo

• Support-vector networks

  Citation: 30656

  Authors: Corinna, Vladimir

• Bagging predictors

  Citation: 13642

  Authors: Leo

• Induction of decision trees

  Citation: 9385

  Authors: J. R.

• Gene Selection for Cancer Classification using Support Vector Machines

  Citation: 7551

  Authors: Isabelle, Jason, Stephen, Vladimir

• Q-learning

  Citation: 7465

  Authors: Christopher J. C. H., Peter

• Support-Vector Networks

  Citation: 7050

  Authors: Corinna, Vladimir

• Extremely randomized trees

  Citation: 6463

  Authors: Pierre, Damien, Louis

• Multitask Learning

  Citation: 4292

  Authors: Rich

• Bayesian Network Classifiers

  Citation: 3589

  Authors: Nir, Dan, Moises

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