Inferring decision trees using the minimum description length principle
Information and Computation
C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
MML Inference of Decision Graphs with Multi-way Joins
AI '02 Proceedings of the 15th Australian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
On growing better decision trees from data
On growing better decision trees from data
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Statistical and Inductive Inference by Minimum Message Length (Information Science and Statistics)
Using evolutionary algorithms for the unit testing of object-oriented software
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Classifier Ensembles with a Random Linear Oracle
IEEE Transactions on Knowledge and Data Engineering
Feature selection via Boolean independent component analysis
Information Sciences: an International Journal
Building classification models from microarray data with tree-based classification algorithms
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
ECML PKDD'11 Proceedings of the 2011 European conference on Machine learning and knowledge discovery in databases - Volume Part II
Decision forests with oblique decision trees
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
A preliminary MML linear classifier using principal components for multiple classes
AI'05 Proceedings of the 18th Australian Joint conference on Advances in Artificial Intelligence
MML mixture models of heterogeneous poisson processes with uniform outliers for bridge deterioration
AI'06 Proceedings of the 19th Australian joint conference on Artificial Intelligence: advances in Artificial Intelligence
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We propose a multivariate decision tree inference scheme by using the minimum message length (MML) principle (Wallace and Boulton, 1968; Wallace and Dowe, 1999) The scheme uses MML coding as an objective (goodness-of-fit) function on model selection and searches with a simple evolution strategy We test our multivariate tree inference scheme on UCI machine learning repository data sets and compare with the decision tree programs C4.5 and C5 The preliminary results show that on average and on most data-sets, MML oblique trees clearly perform better than both C4.5 and C5 on both “right”/“wrong” accuracy and probabilistic prediction – and with smaller trees, i.e., less leaf nodes.