Advances in Data Mining, Machine Learning and Causal Inference and Their Applications
This special issue belongs to the section "E1: Mathematics and Computer Science".
Special Issue Information
Dear Colleagues,
In recent decades, the cores of artificial intelligence, including data mining, machine learning and causal inference, have gained increasing attention across many areas, such as education, economics, health and computer version. A large number of works have promptly been developed in computer science and there have been corresponding applications in engineering, industry, economics, health, biology, and so on. Computational theoretics and methodologies play a critical role in the core of artificial intelligence.
This Special Issue focuses on the theoretical and methodological methods in data science, especially in the cores of artificial intelligence, with topics including but not limited to those in the following fields: computer version, natural language processing, bioinformatics, knowledge graph, knowledge engineering, mathematics, explainable artificial intelligence (XAI), distributed computation, multiagent technology, fuzzy systems, deep learning, causal discovery, causal inference, latent variables, selection bias, and graphical causal modelling.
Dr. Debo Cheng
Dr. Junbo Ma
Dr. Rongyao Hu
Guest Editors
Manuscript Submission Information
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Keywords
- feature extraction/selection or dimensionality reduction and their applications
- knowledge graph, knowledge engineering
- natural language processing
- bioinformatics
- retrieval methods
- supervised/unsupervised/semi-supervised/transfer learning
- computational social science such recommendation system, and persuasive computing
- incremental learning (or online learning)
- data fusion and multi-source multimedia data
- explainable artificial intelligence (XAI)
- causal discovery
- causal inference, such as average causal effect estimation, Heterogeneous causal estimates
- fairness, discrimination detection
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Published Papers
Mathematics - ISSN 2227-7390