Refinement-based disintegration: An approach to re-representation in relational learning

被引：3

作者：

Ontanon, Santiago ^{[1
]}

Plaza, Enric ^{[2
]}

机构：

[1] Drexel Univ, Philadelphia, PA 19104 USA

[2] Spanish Council Sci Res, Artificial Intelligence Res Inst IIIA, Bellaterra, Spain

来源：

AI COMMUNICATIONS | 2015年 / 28卷 / 01期

关键词：

Relational learning; re-representation; refinement operators; feature terms; propositionalization;

D O I：

10.3233/AIC-140621

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：35 / 46

页数：12

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