Paper Info
Title
Detecting features from confusion matrices using generalized formal concept analysis
Abstract
We claim that the confusion matrices of multiclass problems can be analyzed by means of a generalization of Formal Concept Analysis to obtain symbolic information about the feature sets of the underlying classification task We prove our claims by analyzing the confusion matrices of human speech perception experiments and comparing our results to those elicited by experts.
Year
DOI
Venue
2010
10.1007/978-3-642-13803-4_47
HAIS (2)
Keywords
Field
DocType
symbolic information,generalized formal concept analysis,human speech perception experiment,underlying classification task,feature set,confusion matrix,formal concept analysis,detecting feature,multiclass problem,speech perception
Galois connection,Confusion,Confusion matrix,Pattern recognition,Matrix (mathematics),Computer science,Artificial intelligence,Speech perception,Formal concept analysis,Machine learning
Conference
Volume
ISSN
ISBN
6077
0302-9743
3-642-13802-0
Citations 
PageRank 
References 
3
0.41
4
Authors
2
Name
Order
Citations
PageRank
Carmen Peláez-moreno113022.07
Francisco J. Valverde-Albacete211620.84