Abstract
This paper presents a generalized theory for capturing and manipulating classification information. We define decision algebra which models decision-based classifiers as higher order decision functions abstracting from implementations using decision trees (or similar), decision rules, and decision tables. As a proof of the decision algebra concept we compare decision trees with decision graphs, yet another instantiation of the proposed theoretical framework, which implement the decision algebra operations efficiently and capture classification information in a non-redundant way. Compared to classical decision tree implementations, decision graphs gain learning and classification speed up to 20% without accuracy loss and reduce memory consumption by 44%. This is confirmed by experiments.
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Danylenko, A., Lundberg, J., Löwe, W. (2011). Decisions: Algebra and Implementation. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_3
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DOI: https://doi.org/10.1007/978-3-642-23199-5_3
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