A comparison of hierarchical multi-output recognition approaches for anuran classification

• Type: Article in International Scientific Journal
• Date: 2018

Authors

Colonna, JG (Author) Other; João Gama (Author) FEP; Nakamura, EF (Author) Other

Journal

Title: Machine Learning

Vol. 107 No. 11 SI

Pages: 1651-1671

ISSN: 0885-6125

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 12 Citations

Scopus - 16 Citations

Other information

Authenticus ID: P-00P-1DR

DOI: 10.1007/s10994-018-5739-8

Abstract (EN): In bioacoustic recognition approaches, a flat classifier is usually trained to recognize several species of anurans, where the number of classes is equal to the number of species. Consequently, the complexity of the classification function increases proportionally with the number of species. To avoid this issue, we propose a hierarchical approach that decomposes the problem into three taxonomic levels: the family, the genus, and the species. To accomplish this, we transform the original single-labelled problem into a multi-output problem (multi-label and multi-class) considering the biological taxonomy of the species. We then develop a top-down method using a set of classifiers organized as a hierarchical tree. We test and compare two hierarchical methods, using (1) one classifier per parent node and (2) one classifier per level, against a flat approach. Thus, we conclude that it is possible to predict the same set of species as a flat classifier, and additionally obtain new information about the samples and their taxonomic relationship. This helps us to better understand the problem and achieve additional conclusions by the inspection of the confusion matrices at the three classification levels. In addition, we propose a soft decision rule based on the joint probabilities of hierarchy pathways. With this we are able to identify and reject confusing cases. We carry out our experiments using cross-validation performed by individuals. This form of CV avoids mixing syllables that belong to the same specimens in the testing and training sets, preventing an overestimate of the accuracy and generalizing the predictive capabilities of the system. We tested our methods in a dataset with sixty individual frogs, from ten different species, eight genera, and four families, achieving a final Macro-Fscore of 80 and 70% with and without applying the rejection rule, respectively.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 21