Paper Info
Title
Evaluation Strategies for Learning Algorithms of Hierarchies.
Abstract
Several learning tasks comprise hierarchies. Comparison with a "gold-standard" is often performed to evaluate the quality of a learned hierarchy. We assembled various similarity metrics that have been proposed in different disciplines and compared them in a unified interdisciplinary framework for hierarchical evaluation which is based on the distinction of three fundamental dimensions. Identifying deficiencies for measuring structural similarity, we suggest three new measures for this purpose, either extending existing ones or based on new ideas. Experiments with an artificial dataset were performed to compare the different measures. As shown by our results, the measures vary greatly in their properties.
Year
DOI
Venue
2008
10.1007/978-3-642-01044-6_7
ADVANCES IN DATA ANALYSIS, DATA HANDLING AND BUSINESS INTELLIGENCE
Keywords
Field
DocType
Clustering,Gold-standard evaluation,Hierarchy,Ontology learning
Semantic similarity,Similarity heuristic,Artificial intelligence,Hierarchy,Cluster analysis,Machine learning,Ontology learning,Mathematics
Conference
ISSN
Citations 
PageRank 
1431-8814
4
0.48
References 
Authors
11
2
Name
Order
Citations
PageRank
Korinna Bade1788.37
Dominik Benz250021.61