Paper Info
Title
Region Classification with Decision Trees
Abstract
The region-classification task is to construct class regions containing the correct classes of the objects being classified with a given probability. To turn a point classifier into a region classifier, the conformal framework is used \cite{papadopoulos,vovk2008,vovk2005}. However, applying the framework requires a non-conformity function. This function estimates the instances' non-conformity for the point classifier used. This paper studies how to turn decision trees into region classifiers. It considers two non-conformity functions. The first one is a general non-conformity function applicable to any point classifier \cite{papadopoulos,vovk2008,vovk2005}. The second function is a specific non-conformity function for decision trees \cite{vovk2005}. Our main contribution is twofold. First we show, contrary to \cite{vovk2005}, that the general function outperforms the specific one for decision-tree region classifiers in terms of validity and efficiency of the class regions. Second, we show how the decision-tree complexity influences the quality of the class regions based on these two functions.
Year
DOI
Venue
2008
10.1109/ICDMW.2008.19
ICDM Workshops
Keywords
Field
DocType
image classification,computational complexity,algorithm design and analysis,decision tree,data mining,training data,generating function,classification algorithms,decision trees
Decision tree,Data mining,Computer science,Artificial intelligence,Classifier (linguistics),Contextual image classification,Training set,Algorithm design,Pattern recognition,Conformal map,Statistical classification,Machine learning,Computational complexity theory
Conference
Citations 
PageRank 
References 
0
0.34
10
Authors
2
Name
Order
Citations
PageRank
J. van Prehn100.34
Evgueni N. Smirnov22420.38