C4.5: programs for machine learning
C4.5: programs for machine learning
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Machine Learning
Machine Learning
The Alternating Decision Tree Learning Algorithm
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Option Decision Trees with Majority Votes
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
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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.