Recognizing Family, Genus, and Species of Anuran Using a Hierarchical Classification Approach

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

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

Conference proceedings International

Title: DISCOVERY SCIENCE, (DS 2016)

Pages: 198-212

19th International Conference on Discovery Science (DS) / 27th International Conference on Algorithmic Learning Theory (ALT)

Bari, ITALY, OCT 19-21, 2016

Indexing

ISI Web of Knowledge - 10 Citations

Scopus - 10 Citations

Other information

Authenticus ID: P-00M-3X5

DOI: 10.1007/978-3-319-46307-0_13

Abstract (EN): In bioacoustic recognition approaches, a "flat" classifier is usually trained to recognize several species of anuran, where the number of classes is equal to the number of species. Consequently, the complexity of the classification function increases proportionally to the amount 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 level. To accomplish this, we transform the original single-label problem into a multi-dimensional problem (multi-label and multi-class) considering the Linnaeus taxonomy. Then, we develop a top-down method using a set of classifiers organized as a hierarchical tree. Thus, 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 understand the problem better and achieve additional conclusions by the inspection of the confusion matrices at the three levels of classification. In addition, we carry out our experiments using a 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 system in a dataset with sixty individual frogs, from ten different species, eight genus, and four families, achieving a final Micro-and Average-accuracy equal to 86% and 62% respectively.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 15