Files and links (1)
Published, Version of Record (VoR)Open Access (License Unspecified), Open
Journal article
Refinement-based disintegration: An approach to re-representation in relational learning
Ai communications, v 28(1), pp 35-46
01 Jan 2015
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
We present a new approach lo learn from relational data based on re-representation of the examples. This approach, called property-based re-representation is based on a new analysis of the structure of refinement graphs used in ILP and relational learning in general. This analysis allows the characterization of relational examples by a set of multi-relational patterns called properties. Using them, we perform a property-based re-representation of relational examples that facilitates the development of relational learning techniques. Additionally, we show the usefulness of re-representation with a collection of experiments in the context of nearest neighbor classification.
Metrics
Details
- Title
- Refinement-based disintegration: An approach to re-representation in relational learning
- Creators
- Santiago Ontanon - Drexel Univ, Philadelphia, PA 19104 USAEnric Plaza - Research Institute for Artificial Intelligence
- Publication Details
- Ai communications, v 28(1), pp 35-46
- Publisher
- Ios Press
- Number of pages
- 12
- Grant note
- 2009-SGR-1434 / Generalitat de Catalunya; General Electric TIN2012-38450-C03-03 / Project Cognitio TIN2009-13692-C03-01 / Project Next-CBR
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Computer Science
- Web of Science ID
- WOS:000346283600004
- Scopus ID
- 2-s2.0-84918594032
- Other Identifier
- 991019167898304721
UN Sustainable Development Goals (SDGs)
This publication has contributed to the advancement of the following goals:
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence