An Efficient Approximation to Lookahead in Relational Learners

Abstract. 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.

• Type: Conference
• Year: 2006
• Where: ECML
• Authors: Jan Struyf, Jesse Davis, C. David Page Jr.
• Keywords: ECML 2006, Greedy, Greedy Search, Greedy Search Misses, Machine Learning