Name
Abstract | ||
|---|---|---|
Using an ensemble of classifiers instead of a single classifier has been shown to improve generalization performance in many
pattern recognition problems. However, the extent of such improvement depends greatly on the amount of correlation among the
errors of the base classifiers. Therefore, reducing those correlations while keeping the classifiers’ performance levels high
is an important area of research. In this article, we explore Input Decimation (ID), a method which selects feature subsets
for their ability to discriminate among the classes and uses these subsets to decouple the base classifiers. We provide a
summary of the theoretical benefits of correlation reduction, along with results of our method on two underwater sonar data
sets, three benchmarks from the Probenl/UCI repositories, and two synthetic data sets. The results indicate that input decimated
ensembles outperform ensembles whose base classifiers use all the input features; randomly selected subsets of features; and
features created using principal components analysis, on a wide range of domains. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1007/s10044-002-0181-7 | Pattern Anal. Appl. |
Keywords | Field | DocType |
Key words: Classification,Combining classifier,Correlation reduction,Dimensionality reduction,Ensembles,Feature selection | Data mining,Data set,Dimensionality reduction,Feature selection,Random subspace method,Artificial intelligence,Classifier (linguistics),Artificial neural network,Decimation,Pattern recognition,Principal component analysis,Mathematics,Machine learning | Journal |
Volume | Issue | ISSN |
6 | 1 | 1433-7541 |
Citations | PageRank | References |
31 | 2.00 | 42 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| kagan tumer | 1 | 1632 | 168.61 |
| nikunj c oza | 2 | 694 | 54.32 |
| daniel clancy | 3 | 96 | 7.30 |