Identification of Snail Species applying Instance-Based Learning

• Gustavo Silva Semaan INFES / UFF
• Marcelo Nocelle Almeida INFES / UFF
• Raphael Magno de Souza Lage INFES / UFF
• Marcos Vinicius Naves Bedo INFES / UFF
• Debora Alvernaz Corrêa UniAmérica University Center
• José André de Moura Brito ENCE / IBGE

DOI: https://doi.org/10.5753/eri-rj.2021.18771

Abstract

Decision-support systems benefit from hidden patterns (semi-)automatically extracted from massive digital data. In the specific domain of gastropod characterization, morphological data and measurements can support biologists in the identification of land snails by patterns. Although slugs and snails can be easily identified by their excretory and reproductive systems, the mollusk body is commonly inaccessible because of either soft material deterioration or shell fossilization. This study aims at investigating the behavior of a distance-based classifier algorithm over a handcrafted dataset of morphological features of land snail shells. Experimental evaluations indicate a fine-tuned distance-based classifier achieved a hit ratio up to 99% in the task of snail identification.

Keywords: Decision Support Systems, Classification, Snails

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