C4.5: programs for machine learning
C4.5: programs for machine learning
Relational Data Mining
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Generalized skewing for functions with continuous and nominal attributes
ICML '05 Proceedings of the 22nd international conference on Machine learning
The relationship between Precision-Recall and ROC curves
ICML '06 Proceedings of the 23rd international conference on Machine learning
Improving the efficiency of inductive logic programming through the use of query packs
Journal of Artificial Intelligence Research
Top-down induction of first-order logical decision trees
Artificial Intelligence
An integrated approach to learning bayesian networks of rules
ECML'05 Proceedings of the 16th European conference on Machine Learning
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Greedy machine learning algorithms suffer from shortsightedness, potentially returning suboptimal models due to limited exploration of the search space. Greedy search misses useful refinements that yield a significant gain only in conjunction with other conditions. Relational learners, such as inductive logic programming algorithms, are especially susceptible to this problem. Lookahead helps greedy search overcome myopia; unfortunately it causes an exponential increase in execution time. Furthermore, it may lead to overfitting. We propose a heuristic for greedy relational learning algorithms that can be seen as an efficient, limited form of lookahead. Our experimental evaluation shows that the proposed heuristic yields models that are as accurate as models generated using lookahead. It is also considerably faster than lookahead.