Explainable Classification Methods for Fish Species Detection Using Hydroacoustic Data

This work aims to evaluate explainable classification methods for the detection of fish species from hydroacoustic data acquired by echo sounders at a region near the coastline of south and southeastern Brazil. Decision trees and fuzzy rule-based methods were adopted. The fitted models were evaluate...

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Bibliographic Details
Published in:	2021 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) pp. 1 - 6
Main Authors:	Bonifacio, Lucas T., Lucca, Giancarlo, Dimuro, Gracaliz Pereira, Borges, Eduardo N., Emmendorfer, Leonardo R., Weigert, Stefan Cruz
Format:	Conference Proceeding
Language:	English
Published:	IEEE 11-07-2021
Subjects:	Analytical models Classification algorithms Conferences Data models Decision trees explanability Fish fish classification fuzzy rule-based classification systems hydroacoustics Linguistics
Online Access:	Get full text
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Summary:	This work aims to evaluate explainable classification methods for the detection of fish species from hydroacoustic data acquired by echo sounders at a region near the coastline of south and southeastern Brazil. Decision trees and fuzzy rule-based methods were adopted. The fitted models were evaluated by quality measures based on the performance of the classifiers and also by an expert which analyzed the usefulness of the rules on describing the schools. The models learned by the algorithms performed well for the available data and were able to represent the documented behavior of the species considered in the studied region, according to the literature.
ISSN:	1558-4739
DOI:	10.1109/FUZZ45933.2021.9494446