C4.5: programs for machine learning
C4.5: programs for 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
Discriminant Adaptive Nearest Neighbor Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lazy learning
Machine Learning
Option Decision Trees with Majority Votes
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Hi-index | 0.00 |
This paper proposes a new supervised induction algorithm, IGR, that uses each training instances as a guide of rule induction. IGR learns a set of if-then rules by inducing a pseudo-optimun classification rule for each training instance. IGR weighs the induced rules by using the number of trianing instances covered by them and classifies new instances by majority voting with the weights. Experimentalresu lts with twenty datasets in UCI repository show IGR can induce more accurate classification rules than existing learning algorithms such as C4.5, AQ and LazyDT. The experiments also show that IGR does not generate too many rules even if it is applied to large problems.