Name
Abstract | ||
|---|---|---|
In this paper we present a method of data decomposition to avoid the necessity of reasoning on data with missing attribute values. The original incomplete data is decomposed into data subsets without missing values. Next, methods for classifier induction are applied to such sets. Finally, a conflict resolving method is used to combine partial answers from classifiers to obtain final classification. We provide an empirical evaluation of the decomposition method with use of various decomposition criteria. |
Year | Venue | Keywords |
|---|---|---|
2002 | Rough Sets and Current Trends in Computing | incomplete data decomposition,data subsets,decomposition method,partial answer,missing value,classifier induction,various decomposition criterion,original incomplete data,empirical evaluation,missing attribute value,final classification,artificial intelligence,conflict resolution,classification,missing data,incomplete information |
Field | DocType | Volume |
Data mining,Decision table,Computer science,Conflict resolution,Decomposition method (constraint satisfaction),Artificial intelligence,Missing data,Data decomposition,Classifier (linguistics),Complete information,Machine learning | Conference | 2475 |
ISSN | ISBN | Citations |
0302-9743 | 3-540-44274-X | 2 |
PageRank | References | Authors |
0.43 | 5 | 1 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rafal Latkowski | 1 | 22 | 2.33 |