Name
Abstract | ||
|---|---|---|
This paper proposes a novel algorithm for multiple classifiers combination based on clustering and selection technique (called M3CS), which can find in the feature space the regions where each classifier has best classification performance. The proposed method may be divided into two steps: clustering and selection (operation). At clustering step, the feature space is partitioned into several regions by clustering separately the correctly and incorrectly classified training samples from each classifier, and the performances of the classifier in each region are calculated. In the selection step, the most accurate classifier in the vicinity of the input sample is nominated to provide the final decision of the committee. The performance comparison between M3CS and Kuncheva's CS+DT method, as well as some simple aggregation methods such as maximum, minimum, average, and majority vote, confirms the validity of the proposed scheme. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1016/S1566-2535(01)00033-1 | Information Fusion |
Keywords | Field | DocType |
Classifier combination,Classifier selection,Clustering | Data mining,Fuzzy clustering,Random subspace method,Artificial intelligence,Cluster analysis,Classifier (linguistics),Canopy clustering algorithm,Pattern recognition,Correlation clustering,Margin classifier,Mathematics,Machine learning,Quadratic classifier | Journal |
Volume | Issue | ISSN |
2 | 3 | 1566-2535 |
Citations | PageRank | References |
37 | 1.35 | 10 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rujie Liu | 1 | 147 | 15.49 |
| Baozong Yuan | 2 | 485 | 48.29 |