Multi Label Learning Approaches for Multi Species Avifaunal Occurrence Modelling: A Case Study of South Eastern Tamil Nadu

Abstract

Many multi label problem transformation (PT) and algorithm adaptation (AA) methods need to be explored to get good candidate for avifaunal occupancy modelling. This research contrasted eight commonly used state-of-the-art PT and AA multi label methods. The data was created by collecting January 2014-December 2014 records from e-bird repository for the study area Madurai district, south eastern Tamil Nadu. The analysis shows that classifier chain (CC) and multi label naive Bayes (MLNB) are the good aspirants for avifauna data. The MLNB did best with 0.019 hamming loss and 90% average precision. To the best of our knowledge this is the first time to use MLNB for avifaunal data and the results of multi label naive Bayes concludes that out of 143 species observed, six species had high occurrence rate and 68 species had low occurrence rate.