Paper Info
Title
Data Decomposition and Decision Rule Joining for Classification of Data with Missing Values.
Abstract
In this paper we present a new approach to handling incomplete information and classifier complexity reduction. We describe a method, called D(3)RJ, that performs data decomposition and decision rule joining to avoid the necessity of reasoning with missing attribute values. In the consequence more complex reasoning process is needed than in the case of known algorithms for induction of decision rules. The original incomplete data table is decomposed into sub-tables without missing values. Next, methods for induction of decision rules are applied to these sets. Finally, an algorithm for decision rule joining is used to obtain the final rule set from partial rule sets. Using D(3)RJ method it is possible to obtain smaller set of rules and next better classification accuracy than classic decision rule induction methods. We provide an empirical evaluation of the D(3)RJ method accuracy and model size on data with missing values of natural origin.
Year
DOI
Venue
2004
10.1007/978-3-540-25929-9_30
Lecture Notes in Computer Science
Keywords
DocType
Volume
missing values,decision rule,complexity reduction
Conference
3100
ISSN
Citations 
PageRank 
0302-9743
17
0.79
References 
Authors
27
2
Name
Order
Citations
PageRank
Rafal Latkowski1222.33
Michal Mikolajczyk2242.08