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 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.This study was supported by the Spanish Ministry of Science (TIN2016-77356-P, PID2019-108392GB-I00 AEI/10.13039/501100011033), PNPD/CAPES (464880/2019-00), CNPq (301618/2019-4), FAPERGS (19/2551-0001660), and CAPES Financial Code 001