Probabilistic Decision Trees
Probabilistic Decision Trees is a machine learning model that extends traditional decision trees by incorporating probabilistic transitions at nodes, enabling the representation of uncertainty in both predictions and structure through probability distributions over class labels or outcomes.
79
Publications
5.3K
Citations
223
Authors
124
Institutions
Publications per year
2017–2026
26
Variants
Stochastic Decision Trees
Parents
79
Data mining: concepts, models and techniques
Choice Reviews Online · 2011 · 558 citations
Data Mining with Decision Trees: Theory and Applications
Online Information Review · 2015 · 451 citations
Combining biological networks to predict genetic interactions
Sharyl L. Wong, Lan V. Zhang, Amy H.Y. Tong et al. · Proceedings of the National Academy of Sciences · 2004 · 254 citations · Full text
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124

University of Exeter
Exeter, United Kingdom
3 h-index
15 publications
498 citations

Universidad de Granada
Granada, Spain
3 h-index
6 publications
343 citations
Hebrew University of Jerusalem
Jerusalem, Israel
3 h-index
3 publications
391 citations

Universidad de Málaga
Málaga, Spain
2 h-index
6 publications
966 citations

Stockholm University
Stockholm, Sweden
2 h-index
4 publications
49 citations

DePaul University
Chicago, United States
2 h-index
7 publications
156 citations

University of Bristol
Bristol, United Kingdom
2 h-index
4 publications
113 citations

Ames Research Center
Mountain View, United States
2 h-index
2 publications
53 citations
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AIM · 2 publications · 274 citations
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Repository · 2 publications · 64 citations
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1 publications · 62 citations
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1 publications · 22 citations
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TECS · 1 publications · 19 citations
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