Bird Classification Using Binary Relevance approach with Random Forest in Python
Bird Classification Using Binary Relevance approach with Random Forest in Python
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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.
