Abstract
Induction of decision trees belongs to the most popular algorithms used in machine learning and data mining. This process will result in a single tree that can be use both for classification of new examples and for description the partitioning of the training set. In the paper we propose an alternative approach that is related to the idea of finding all interesting relations (usually association rules, but in our case all interesting trees) in given data. When building the so called exploration trees, we consider not a single best attribute for branching but more ”good” attributes for each split. The proposed method will be compared with the ”standard” C4.5 algorithm on several data sets from the loan application domain.
We propose this algorithm in the framework of the GUHA method, a genuine exploratory analysis method that aims at finding all patterns, that are true in the analyzed data.
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Berka, P. (2011). ETree Miner: A New GUHA Procedure for Building Exploration Trees. In: Kryszkiewicz, M., Rybinski, H., Skowron, A., Raś, Z.W. (eds) Foundations of Intelligent Systems. ISMIS 2011. Lecture Notes in Computer Science(), vol 6804. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21916-0_11
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DOI: https://doi.org/10.1007/978-3-642-21916-0_11
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