A scalable pairwise class interaction framework for multidimensional classification

DOI10.1016/j.ijar.2015.07.007zbMath1346.68152OpenAlexW2125113417MaRDI QIDQ895535

Thomas D. Nielsen, Jacinto Arias, José A. Gámez, José M. Puerta

Publication date: 3 December 2015

Published in: International Journal of Approximate Reasoning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.ijar.2015.07.007

zbMATH Keywords

Markov random fieldsmultidimensional classificationprobabilistic classifiers

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (3)

A Two-Stage Conditional Random Field Model Based Framework for Multi-Label Classification ⋮ Multi-dimensional Bayesian network classifiers for partial label ranking ⋮ Tractability of most probable explanations in multidimensional Bayesian network classifiers

Uses Software

• MapReduce
• WEKA
• MULAN

Cites Work

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• Multi-dimensional classification with Bayesian networks
• Approximating probabilistic inference in Bayesian belief networks is NP- hard
• Not so naive Bayes: Aggregating one-dependence estimators
• Label ranking by learning pairwise preferences
• Trading off Speed and Accuracy in Multilabel Classification
• A New Method for Vertical Parallelisation of TAN Learning Based on Balanced Incomplete Block Designs
• Expressive Power of Binary Relevance and Chain Classifiers Based on Bayesian Networks for Multi-label Classification
• Bayesian Networks and Decision Graphs
• Inference and Learning in Multi-dimensional Bayesian Network Classifiers
• Approximating discrete probability distributions with dependence trees

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