Application of Improved Boruta Algorithm in Music Emotion Research
Luyin Shao, Binshan Zhao
Journal of Intelligent Computing, Vol. 15, No. 4, 2024.
DOI: https://doi.org/10.6025/jic/2024/15/4/142-148
Link: https://www.dline.info/jic/fulltext/v15n4/jicv15n4_4.pdf
Abstract:
Music can be heard everywhere in the fast-paced and high-stress modern society.
With the development of time, peopleâ€™s demand for music has shifted from simple
leisure entertainment to seeking emotional resonance. To address this, the big
data Boruta algorithm has emerged as an alternative to random forest classification
algorithms, enabling more precise classification results. By re-implementing it, we
applied the LightGBM algorithm to a Turkish music dataset and used random forest,
XGBoost, and LightGBM algorithms for classification prediction. We found their
better applicability to these datasets by evaluating these algorithmsâ€™ accuracy,
Kappa coefficient, and Hamming distance.