| Full text | |
| Author(s): |
Faria, Fabio Augusto
;
Sarkar, Sudeep
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2018 24TH INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR); v. N/A, p. 6-pg., 2018-01-01. |
| Abstract | |
Many works in the literature have used machine learning techniques to solve their classification problems in different knowledge areas, e.g., medicine, agriculture, and remote sensing. Since there is no a single machine learning technique that achieves the best results for all kind of applications, a good alternative is the fusion of classification techniques, also known as multiple classifier systems ({MCS). A common challenge in MCS is the selection of a few classifiers among many classifiers that are available in the literature; using all possible classifiers is not a feasible alternative. The choice of the classifiers becomes an essential factor, i.e., we need an ensemble selection approach. In this work, we propose a novel graph-based approach for static ensemble selection ({GASES) to find or choose the best classifier set for remote sensing image classification. Experiments demonstrate that GASES improves performance by up to 70% over different baseline approaches when fusing classifiers. It decreases the number of classifiers used while retaining the effectiveness of using all of the classifiers. Furthermore, our proposed method is a more straightforward and intuitive technique for static ensemble selection scheme than other baseline approaches such as Consensus and Kendall. (AU) | |
| FAPESP's process: | 10/14910-0 - Evidence-Fusion Methods for Multimedia Retrieval and Classification |
| Grantee: | Fabio Augusto Faria |
| Support Opportunities: | Scholarships in Brazil - Doctorate |