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Relational Instance-Based Learning (1996)
| Venue: | Proceedings of the Thirteenth International Conference on Machine Learning |
| Citations: | 79 - 1 self |
BibTeX
@INPROCEEDINGS{Emde96relationalinstance-based,
author = {Werner Emde and Dietrich Wettschereck},
title = {Relational Instance-Based Learning},
booktitle = {Proceedings of the Thirteenth International Conference on Machine Learning},
year = {1996},
pages = {122--130},
publisher = {Morgan Kaufmann}
}
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Abstract
A relational instance-based learning algorithm, called Ribl, is motivated and developed in this paper. We argue that instancebased methods o#er solutions to the often unsatisfactory behavior of current inductive logic programming #ILP# approaches in domains with continuous attribute values and in domains with noisy attributes and#or examples. Three research issues that emerge when a propositional instance-based learner is adapted to a #rst-order representation are identi#ed: #1# construction of cases from the knowledge base, #2# computation of similaritybetween arbitrarily complex cases, and #3# estimation of the relevance of predicates and attributes. Solutions to these issues are developed. Empirical results indicate that Ribl is able to achieve high classi#cation accuracy in a variety of domains. to appear in: Proc. 13th International Conference on Machine Learning, L. Saitta #ed.#, Morgan Kaufmann, 1996 1 Introduction The #eld of Inductive Logic Programming ...
Keyphrases
relational instance-based learning identi ed research issue empirical result high classi cation accuracy ilp approach propositional instance-based learner relational instance-based learning algorithm continuous attribute value complex case unsatisfactory behavior knowledge base er solution inductive logic programming saitta ed rst-order representation current inductive logic machine learning instancebased method morgan kaufmann international conference noisy attribute
