Explainable Classification Methods for Fish Species Detection Using Hydroacoustic Data

Abstract

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 rulebased methods were adopted. The fitted models were evaluated by quality measures based on the performance of the classifiers and also by an expert which analyzes 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.