Name
Abstract | ||
|---|---|---|
In this paper we have proposed a lazy learning mechanism for multiclass classification using genetic programming. This method is an improvement of traditional binary decomposition method for multiclass classification. We train classifiers for individual classes for a certain number of generations. Individual trained classifiers for each class are combined in a single chromosome. A population of such chromosomes is created and evolved further. This method suppresses the conflicting situations common in binary decomposition method. The proposed lazy learning method has performed better than traditional binary decomposition method over five benchmark datasets taken from UCI ML repository. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-25944-9_23 | ICIC (2) |
Keywords | Field | DocType |
certain number,conflicting situation,lazy learning mechanism,multiclass classification,individual trained classifier,multi-class classification,binary decomposition method,traditional binary decomposition method,genetic programming,proposed lazy learning method,uci ml repository,individual class,expression,classification,classifier,algorithm | Population,Pattern recognition,Computer science,Lazy learning,Binary decomposition,Genetic programming,Artificial intelligence,Classifier (linguistics),Machine learning,Multiclass classification | Conference |
Volume | ISSN | Citations |
6839 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 16 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hajira Jabeen | 1 | 67 | 10.58 |
| Abdul Rauf Baig | 2 | 126 | 15.82 |