Paper Info
Title
Area under the distance threshold curve as an evaluation measure for probabilistic classifiers
Abstract
Evaluation for probabilistic multiclass systems has predominately been done by converting data into binary classes. While effective in quantifying the classifier performance, binary evaluation causes a loss in ability to distinguish between individual classes. We report that the evaluation of multiclass probabilistic classifiers can be quantified by using the area under the distance threshold curve for multiple distance metrics. We construct our classifiers for evaluation with data from the National Cancer Institute (NCI) Lung Image Database Consortium (LIDC) for the semantic characteristic of malignancy. We conclude that the area under the distance threshold curve can provide a measure of the classifier performance when the classifier has more than two classes and probabilistic predictions.
Year
DOI
Venue
2013
10.1007/978-3-642-39712-7_49
MLDM
Keywords
Field
DocType
probabilistic prediction,database consortium,multiclass probabilistic classifier,binary evaluation,evaluation measure,probabilistic multiclass system,distance threshold curve,lung image,classifier performance,binary class,multiple distance metrics,machine learning,k nearest neighbor,roc curve,medical informatics,probabilistic classifier
k-nearest neighbors algorithm,Pattern recognition,Computer science,Artificial intelligence,Image database,Probabilistic logic,Classifier (linguistics),Probabilistic classification,Machine learning,Binary number
Conference
Citations 
PageRank 
References 
0
0.34
10
Authors
4
Name
Order
Citations
PageRank
Sydney Williams100.34
Michael Harris200.34
Jacob D. Furst354556.63
Daniela Stan Raicu446946.22