C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Transversing itemset lattices with statistical metric pruning
PODS '00 Proceedings of the nineteenth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Machine Learning
Option Decision Trees with Majority Votes
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
On growing better decision trees from data
On growing better decision trees from data
XRules: an effective structural classifier for XML data
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
ART: A Hybrid Classification Model
Machine Learning
Top-down induction of first-order logical decision trees
Artificial Intelligence
Tree2: decision trees for tree structured data
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
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Decision trees are among the most effective and interpretable classification algorithms while ensembles techniques have been proven to alleviate problems regarding over-fitting and variance. On the other hand, decision trees show a tendency to lack stability given small changes in the data, whereas interpreting an ensemble of trees is challenging to comprehend. We propose the technique of Ensemble-Treeswhich uses ensembles of rules withinthe test nodes to reduce over-fitting and variance effects. Validating the technique experimentally, we find that improvements in performance compared to ensembles of pruned trees exist, but also that the technique does less to reduce structural instability than could be expected.