Name
Title | ||
|---|---|---|
Feature subspace ensembles: a parallel classifier combination scheme using feature selection |
Abstract | ||
|---|---|---|
In feature selection (FS), different strategies usually lead to different results. Even the same strategy may do so in distinct feature selection contexts.We propose a feature subspace ensemble method, consisting on the parallel combination of decisions from multiple classifiers. Each classifier is designed using variations of the feature representation space, obtained by means of FS. With the proposed approach, relevant discriminative information contained in features neglected in a single run of a FS method, may be recovered by the application of multiple FS runs or algorithms, and contribute to the decision through the classifier combination process. Experimental results on benchmark data show that the proposed feature subspace ensembles method consistently leads to improved classification performance. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-72523-7_27 | MCS |
Keywords | Field | DocType |
feature selection,distinct feature selection context,different strategy,feature subspace ensemble method,parallel classifier combination scheme,classifier combination process,different result,proposed feature subspace ensemble,multiple fs run,fs method,feature representation space | Pattern recognition,Subspace topology,Feature selection,Feature (computer vision),Random subspace method,Artificial intelligence,Classifier (linguistics),Linear classifier,Discriminative model,Machine learning,Mathematics | Conference |
Volume | ISSN | Citations |
4472 | 0302-9743 | 7 |
PageRank | References | Authors |
0.58 | 13 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hugo Silva | 1 | 227 | 30.18 |
| Ana Fred | 2 | 216 | 17.07 |