Ocenjevanje atributov v neuravnoteženih problemih

Abstract

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Language:	Slovenian
Year of publishing:	2011
Typology:	2.11 - Undergraduate Thesis
Organization:	UL FRI - Faculty of Computer and Information Science
Publisher:	[D. Rački]
UDC:	004(043.2)
COBISS:	8631892
Views:	36
Downloads:	1
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Secondary language:	English
Secondary title:	Attribute evaluation on imbalanced data sets
Secondary abstract:	We analyze the performance of attribute evaluation measures on imbalanced datasets at different levels of imbalance. We sample real world datasets at ratios 1:5, 1:10, 1:50, 1:100, 1:500 and 1:1000. We build decision tree models and for each attribute evaluation measure compute AUC with stratified 5x2 cross validation. To test significance of the difference we use Friedman's test. With Nemenyi's test we determine and graphically display the similarities and differences. We find that the best performing measure at unaltered class ratios is MDL, for class ratios 1:5 the best measure is the angular distance. For ratios 1:10 and 1:50 the beast measure is ReliefF and for class ratios 1:100, 1:500 and 1:1000 the best performing measure is information gain. The worst performing measure on all class ratios is accuracy.
Secondary keywords:	machine learning;imbalanced datasets;attribute evaluation;CORElearn;decision trees;computer science;computer and information science;diploma;
File type:	application/pdf
Type (COBISS):	Bachelor thesis/paper
Thesis comment:	Univ. v Ljubljani, Fak. za računalništvo in informatiko
Pages:	66 str.
ID:	23936536