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Generalized decision directed acyclic graphs for classification tasks
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AI
DOI：10.1016/j.ijar.2023.109041.png)
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摘要
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The recent studies present the various perspectives of Formal Concept Analysis from the Artificial Intelligence point of view since it can provide powerful techniques for knowledge representation, classification tasks, recommendation techniques, or data analysis. Formal Concept Analysis can be generally seen as an unsupervised Machine Learning technique that applies mathematical lattice theory to organize data based on objects and their shared attributes. However, the connections of Formal Concept Analysis with supervised learning are also thoroughly investigated. Regarding supervised learning, the learning algorithms of random forests or decision jungles have become popular and powerful Machine Learning ensemble methods. In this paper, we propose a generalization of a classification method based on the generalized decision directed acyclic graphs. We outline and review the recent studies on connections between Formal Concept Analysis and supervised learning. Finally, we present the results of our experiments regarding several datasets from the UCI Machine Learning repository.
Keyword:
Decision jungles
Formal Concept Analysis
Target attribute
Supervised learning
期刊
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3
论文数:
3.0K
被引数:
5.1K
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