Bird Classification Using Binary Relevance approach with Random Forest in Python

Bird Classification Using Binary Relevance approach with Random Forest in Python

ABSTRACT

The goal of the 2013 MLSP Competition is to predict the set of bird species present in audio recordings, collected in field conditions. Real-world audio data presents special difficulties such as simultaneously vocalizing birds, other animal sounds, and background noise. Although the task can be considered as a multi-instance multi-label learning problem, I propose a Binary Relevance approach with Random Forest. The proposed solution achieves 0.956 AUC and ranks 1st place on the Kaggle private leader board.

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