Summary of Construction Of Decision Trees and Acyclic Decision Graphs From Decision Rule Systems, by Kerven Durdymyradov and Mikhail Moshkov
Construction of Decision Trees and Acyclic Decision Graphs from Decision Rule Systems
by Kerven Durdymyradov, Mikhail Moshkov
First submitted to arxiv on: 2 May 2023
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computational Complexity (cs.CC)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper explores the inverse transformation problem between decision trees and systems of decision rules. Decision trees are widely used classifiers, knowledge representations, and algorithms, offering high interpretability for data analysis. While methods exist to transform decision trees into rule systems, the reverse process is not trivial. The study examines the complexity of constructing decision trees and acyclic decision graphs from decision rule systems, as well as the possibility of describing computation paths in these trees rather than building the entire tree. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to turn a set of rules into a decision tree. Decision trees are useful for making predictions and explaining why those predictions were made. They’re also easy to understand. The opposite problem, turning a decision tree back into a set of rules, is more complicated. This study tries to figure out how hard it is to do this transformation, and if we can just describe the path that the tree takes instead of building the whole thing. |
Keywords
» Artificial intelligence » Decision tree
