Information Processing Letters
International Journal of Man-Machine Studies - Special Issue: Knowledge Acquisition for Knowledge-based Systems. Part 5
Quantifying inductive bias: AI learning algorithms and Valiant's learning framework
Artificial Intelligence
Classifier Systems and the Animat Problem
Machine Learning
Machine Learning
Machine Learning
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Iterative feature construction for improving inductive learning algorithms
Expert Systems with Applications: An International Journal
On preprocessing data for financial credit risk evaluation
Expert Systems with Applications: An International Journal
Adding domain knowledge to SBL through feature construction
AAAI'90 Proceedings of the eighth National conference on Artificial intelligence - Volume 2
Discrimination-based constructive induction of logic programs
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
The feature selection problem: traditional methods and a new algorithm
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
| Hi-index | 0.01 |
We investigate the problem of learning DNF concepts from examples using decision trees as a concept description language. Due to the replication problem, DNF concepts do not always have a concise decision tree description when the tests at the nodes are limited to the initial attributes. However, the representational complexity may be overcome by using high level attributes as tests. We present a novel algorithm that modifies the initial bias determined by the primitive attributes by adaptively enlarging the attribute set with high level attributes. We show empirically that this algorithm outperforms a standard decision tree algorithm for learning small random DNF with and without noise, when the examples are drawn from the uniform distribution.