Classificação de sementes aquáticas do Pantanal Brasileiro utilizando árvores de decisão

André Sol, Edilaine Gonçalves Costa Faria, Daniel Café, Francisco Assis de Oliveira Nascimento

DOI: 10.14209/sbrt.2022.1570822835

Evento: XL Simpósio Brasileiro de Telecomunicações e Processamento de Sinais (SBrT2022)

Keywords:

Abstract

This paper propose a method for aquatic seed classificaction. These seeds are taken from seed banks of the Brazilian Pantanal. Twelve species were selected, each with 20 samples. The data bank has 240 images. Those images are processed into a vector with 226 attributes. The proposed methods are Naive-Bayes, Logistic Regression, Decision Tree, Extra-Trees, Multi-class AdaBoost, Histogram-based Gradient Boosting and Ensable Extra-Trees. Among these algorithms, the Ensable Extra-Trees achieved the higher accuracy score, reaching 98.12%.

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