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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Automatic Meta-Feature Engineering for CNN Fusion in Aerial Scene Classification Task

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Author(s):
de Melo, Vinicius V. [1] ; Sotto, Leo F. D. P. [1] ; Leonardo, Matheus M. [1] ; Faria, Fabio A. [1]
Total Authors: 4
Affiliation:
[1] Univ Fed Sao Paulo, Inst Sci & Technol, BR-04021001 Sao Jose Dos Campos - Brazil
Total Affiliations: 1
Document type: Journal article
Source: IEEE Geoscience and Remote Sensing Letters; v. 17, n. 9, p. 1652-1656, SEPT 2020.
Web of Science Citations: 0
Abstract

The aerial scene-classification task is a challenging problem to remote sensing area with important applicability to civil and military affairs. A technique that has achieved excellent results in this task is the convolutional neural network (CNN). CNNs are powerful semantic-level feature-extraction techniques successfully applied to many application domains. Nevertheless, many works in the literature have shown that a single CNN cannot solve all kinds of application domains properly. Hence, an alternative solution might be the joining of CNN architectures as an ensemble of classifiers. In this sense, this letter proposes a new strategy of deep feature-based classifier fusion through a meta-feature engineering approach based on the Kaizen programming (KP) technique for the aerial scene-classification task. KP is a technique that continuously improves partial solutions and combines them into a complete solution. In the context, a partial solution is a meta-feature, and a complete solution is an ensemble of classifiers. In our experiments on three different public data sets, we show that KP can automatically engineer meta-features that significantly improve the performance of a stacked classifier while reducing the number of total meta-features. (AU)

FAPESP's process: 18/13202-4 - Development of a linear genetic programming algorithm using an estimation of distribution algorithm applied to supervised machine learning
Grantee:Léo Françoso Dal Piccol Sotto
Support Opportunities: Scholarships abroad - Research Internship - Doctorate (Direct)
FAPESP's process: 16/07095-5 - Development of the probabilistic linear genetic programming technique and application on Kaizen programming for supervised machine learning
Grantee:Léo Françoso Dal Piccol Sotto
Support Opportunities: Scholarships in Brazil - Doctorate (Direct)