Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Boolean Feature Discovery in Empirical Learning
Machine Learning
The Use of Background Knowledge in Decision Tree Induction
Machine Learning
C4.5: programs for machine learning
C4.5: programs for machine learning
Trading Accuracy for Simplicity in Decision Trees
Machine Learning
Branching on attribute values in decision tree generation
AAAI '94 Proceedings of the twelfth national conference on Artificial intelligence (vol. 1)
Machine Learning
Building Hierarchical Classifiers Using Class Proximity
VLDB '99 Proceedings of the 25th International Conference on Very Large Data Bases
Hierarchical Classification of Documents with Error Control
PAKDD '01 Proceedings of the 5th Pacific-Asia Conference on Knowledge Discovery and Data Mining
Learning accurate and concise naïve Bayes classifiers from attribute value taxonomies and data
Knowledge and Information Systems
Hierarchical classification of HTML documents with WebClassII
ECIR'03 Proceedings of the 25th European conference on IR research
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Given a set of training examples S and a tree-structured attribute x, the goal in this work is to find a multiple-split test defined on x that maximizes Quinlan's gain-ratio measure. The number of possible such multiple-split tests grows exponentially in the size of the hierarchy associated with the attribute. It is, therefore, impractical to enumerate and evaluate all these tests in order to choose the best one. We introduce an efficient algorithm for solving this problem that guarantees maximizing the gain-ratio over all possible tests. For a training set of m examples and an attribute hierarchy of height d, our algorithm runs in time proportional to dm, which makes it efficient enough for practical use.