Estimation-Based Search Space Traversal in PILP Environments

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Real, JC (Author) Other; Ines Dutra (Author) FCUP; Ricardo Rocha (Author) FCUP

Conference proceedings International

Title: Inductive Logic Programming - 26th International Conference, ILP 2016, London, UK, September 4-6, 2016, Revised Selected Papers

Pages: 1-13

Indexing

Scopus - 3 Citations

Scientific classification

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: P-00M-YHB

DOI: 10.1007/978-3-319-63342-8_1

Abstract (EN): Probabilistic Inductive Logic Programming (PILP) systems extend ILP by allowing the world to be represented using probabilistic facts and rules, and by learning probabilistic theories that can be used to make predictions. However, such systems can be inefficient both due to the large search space inherited from the ILP algorithm and to the probabilistic evaluation needed whenever a new candidate theory is generated. To address the latter issue, this work introduces probability estimators aimed at improving the efficiency of PILP systems. An estimator can avoid the computational cost of probabilistic theory evaluation by providing an estimate of the value of the combination of two subtheories. Experiments are performed on three real-world datasets of different areas (biology, medical and web-based) and show that, by reducing the number of theories to be evaluated, the estimators can significantly shorten the execution time without losing probabilistic accuracy. © Springer International Publishing AG 2017.

Language: English

Type (Professor's evaluation): Scientific