.png){kind=icon}
.png){kind=icon}
Return
Refinement operators for directed labeled graphs with applications to instance-based learning
.png){kind=icon}
PRE
AI
DOI：10.1016/j.knosys.2018.08.006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This paper presents a collection of refinement operators for directed labeled graphs (DLGs), and a family of distance and similarity measures based on them. We build upon previous work on refinement operators for other representations such as feature terms and description logic models. Specifically, we present eight refinement operators for DLGs, which will allow for the adaptation of three similarity measures to DLGs: the anti-unification-based, S-lambda, the property-based, S-pi, and the weighted property-based, S-w pi, similarities. We evaluate the resulting measures empirically, comparing them to existing similarity measures for structured data in the context of instance-based machine learning. (C) 2018 Elsevier B.V. All rights reserved.
Keywords:
Similarity assessment
Refinement operators
Directed labeled graphs
Distance measures
Instance-based learning
Case-based reasoning
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.