Paper Info
Title
Sequential Classification by Exploring Levels of Abstraction.
Abstract
In the paper we describe a sequential classification scheme that iteratively explores levels of abstraction in the description of examples. These levels of abstraction represent attribute values of increasing precision. Specifically, we assume attribute values constitute an ontology (i.e., attribute value ontology) reflecting a domain-specific background knowledge, where more general values subsumes more precise ones. While there are approaches that consider levels of abstraction during learning, the novelty of our proposal consists in exploring levels of abstraction when classifying new examples. The described scheme is essential when tests that increase precision of example description are associated with costs – such a situation is often encountered in medical diagnosis. Experimental evaluation of the proposed classification scheme combined with ontological Bayes classifier (i.e., a näıve Bayes classifier expanded to handle attribute value ontologies) demonstrates that the classification accuracy obtained at higher levels of abstraction (i.e., more general description of classified examples) converges very quickly to the classification accuracy for classified examples represented precisely. This finding indicates we should be able to reduce the number of tests and thus limit their cost without deterioration of the prediction accuracy.
Year
DOI
Venue
2014
10.1016/j.procs.2014.08.111
Procedia Computer Science
Keywords
Field
DocType
sequential classification,levels of abstraction,attribute value ontology,naïve Bayes classifier
Ontology (information science),Data mining,Ontology,Abstraction,Naive Bayes classifier,Computer science,Classification scheme,Artificial intelligence,Novelty,Machine learning,Bayes classifier,Medical diagnosis
Conference
Volume
ISSN
Citations 
35
1877-0509
1
PageRank 
References 
Authors
0.35
12
2
Name
Order
Citations
PageRank
Tomasz Łukaszewski1516.55
Szymon Wilk246140.94