Paper Info
Title
Complexity Of Rule Sets Induced From Data Sets With Many Lost And Attribute-Concept Values
Abstract
In this paper we present experimental results on rule sets induced from 12 data sets with many missing attribute values. We use two interpretations of missing attribute values: lost values and attribute-concept values. Our main objective is to check which interpretation of missing attribute values is better from the view point of complexity of rule sets induced from the data sets with many missing attribute values. The better interpretation is the attribute-value. Our secondary objective is to test which of the three probabilistic approximations used for the experiments provide the simplest rule sets: singleton, subset or concept. The subset probabilistic approximation is the best, with 5% significance level.
Year
DOI
Venue
2016
10.1007/978-3-319-39384-1_3
ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING, (ICAISC 2016), PT II
Keywords
Field
DocType
Incomplete data, Lost values, Attribute, concept values, Probabilistic approximations, MLEM2 rule induction algorithm
Data mining,Data set,Computer science,Probabilistic logic,Singleton
Conference
Volume
ISSN
Citations 
9693
0302-9743
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Patrick G. Clark19113.02
Cheng Gao2128.29
Jerzy W. Grzymala-Busse32196251.27