Name
Abstract | ||
|---|---|---|
This paper introduces a two-stage strategy for multi-class classification problems. The proposed technique is an advancement of tradition binary decomposition method. In the first stage, the classifiers are trained for each class versus the remaining classes. A modified fitness value is used to select good discriminators for the imbalanced data. In the second stage, the classifiers are integrated and treated as a single chromosome that can classify any of the classes from the dataset. A population of such classifier-chromosomes is created from good classifiers (for individual classes) of the first phase. This population is evolved further, with a fitness that combines accuracy and conflicts. The proposed method encourages the classifier combination with good discrimination among all classes and less conflicts. The two-stage learning has been tested on several benchmark datasets and results are found encouraging. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.neucom.2012.01.048 | Neurocomputing |
Keywords | Field | DocType |
Classification,Genetic programming,Classifier,Expression,Rule,Algorithm | Population,Classification rule,Pattern recognition,Random subspace method,Computer science,Binary decomposition,Genetic programming,Artificial intelligence,Classifier (linguistics),Machine learning,Multiclass classification | Journal |
Volume | ISSN | Citations |
116 | 0925-2312 | 7 |
PageRank | References | Authors |
0.47 | 29 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hajira Jabeen | 1 | 67 | 10.58 |
| Abdul Rauf Baig | 2 | 126 | 15.82 |