Estimation-Based Search Space Traversal in PILP Environments

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2016

Autores

Real, JC (Autor) Outra; Ines Dutra (Autor) FCUP; Ricardo Rocha (Autor) FCUP

Ata de Conferência Internacional

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

Páginas: 1-13

Indexação

Scopus - 3 Citações

Classificação Científica

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica