Name
Abstract | ||
|---|---|---|
Ensemble pruning is an important task in supervised learning because of the performance and efficiency advantage it begets to predictive modelling. Performance based empirical comparison (primarily on accuracy) is the most common modus operandi for critical evaluation of ensembles pruned by different algorithms. Deep analysis of existing literature reveals that ensemble size is an ignored attribute while judging the quality of ensembles. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1016/j.patcog.2016.03.017 | Pattern Recognition |
Keywords | Field | DocType |
Ensemble,Ensemble pruning,Cost-effectiveness | Empirical comparison,Data mining,Parameterized complexity,Accrual,CLARITY,Pattern recognition,Computer science,Supervised learning,Artificial intelligence,Predictive modelling,Mathematical properties,Machine learning | Journal |
Volume | Issue | ISSN |
57 | C | 0031-3203 |
Citations | PageRank | References |
0 | 0.34 | 38 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Manju Bhardwaj | 1 | 19 | 2.33 |
| Vasudha Bhatnagar | 2 | 181 | 17.69 |
| Kapil K. Sharma | 3 | 106 | 16.98 |