Bayesian-based instance weighting techniques for instance-based learners

Authors:
Khalil El Hindi;Bayan Abu Shawar
Affiliations:
Department of Computer Science, Faculty of Computer Science, Arab Open University, Amman, Jordan;Department of Computer Science, Faculty of Computer Science, Arab Open University, Amman, Jordan
Venue:
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2012

Citing 5
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Abstract

Instance-Based learners such as the kNN algorithm classify a new instance based on the k most similar instances. Usually these instances have equal weights or votes. Some systems assign them weights that are inversely proportional to their distance from the new instance. In this work, we present several Bayesian-based instance weighting technique that are more suitable for noisy data sets. We use the Naïve Bayesian probability that an instance truly belongs to its class or does not belong to another class, to calculate its weight. Our empirical study shows that these weighting techniques make the kNN algorithm far less sensitive to noisy training data.