Machine learning
On the induction of decision trees for multiple concept learning
On the induction of decision trees for multiple concept learning
Intelligent modeling, diagnosis and control of manufacturing processes
An Information Theoretic Approach to Rule Induction from Databases
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Machine Learning
On Efficient Construction of Decision Trees from Large Databases
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Efficient SQL-querying method for data mining in large data bases
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
International Journal of Approximate Reasoning
Effective probability forecasting for time series data using standard machine learning techniques
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
Using kNN model for automatic feature selection
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
A hyper-heuristic evolutionary algorithm for automatically designing decision-tree algorithms
Proceedings of the 14th annual conference on Genetic and evolutionary computation
On Efficient Handling of Continuous Attributes in Large Data Bases
Fundamenta Informaticae
Fundamenta Informaticae
From Optimal Hyperplanes to Optimal Decision Trees
Fundamenta Informaticae
Software effort prediction: a hyper-heuristic decision-tree based approach
Proceedings of the 28th Annual ACM Symposium on Applied Computing
Automatic design of decision-tree algorithms with evolutionary algorithms
Evolutionary Computation
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We address the problem of selecting an attribute and some of its values for branching during the top-down generation of decision trees. We study the class of impurity measures, members of which are typically used in the literature for selecting attributes during decision tree generation (e.g. entropy in ID3, GID3*, and CART; Gini Index in CART). We argue that this class of measures is not particularly suitable for use in classification learning. We define a new class of measures, called C-SEP, that we argue is better suited for the purposes of class separation. A new measure from C-SEP is formulated and some of its desirable properties are shown. Finally, we demonstrate empirically that the new algorithm, O-BTree, that uses this measure indeed produces better decision trees than algorithms that use impurity measures.