Effect of Feature Selection on the Accuracy of Music Popularity Classification Using Machine Learning Algorithms

被引:22
|
作者
Khan, Faheem [1 ]
Tarimer, Ilhan [2 ]
Alwageed, Hathal Salamah [3 ]
Karadag, Buse Cennet [2 ]
Fayaz, Muhammad [4 ]
Abdusalomov, Akmalbek Bobomirzaevich [1 ]
Cho, Young-Im [1 ]
机构
[1] Gachon Univ, Dept Comp Engn, Seongnam 13120, South Korea
[2] Mugla Sitki Kocman Univ, Dept Informat Syst Engn, TR-48000 Mugla, Turkey
[3] Jouf Univ, Coll Comp & Informat Sci, Sakaka 42421, Saudi Arabia
[4] Univ Cent Asia, Dept Comp Sci, Naryn 722918, Kyrgyzstan
关键词
Spotify datasets (API); !text type='python']python[!/text; data preprocessing; machine learning; music trend;
D O I
10.3390/electronics11213518
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This research aims to analyze the effect of feature selection on the accuracy of music popularity classification using machine learning algorithms. The data of Spotify, the most used music listening platform today, was used in the research. In the feature selection stage, features with low correlation were removed from the dataset using the filter feature selection method. Machine learning algorithms using all features produced 95.15% accuracy, while machine learning algorithms using features selected by feature selection produced 95.14% accuracy. The features selected by feature selection were sufficient for classification of popularity in established algorithms. In addition, this dataset contains fewer features, so the computation time is shorter. The reason why Big O time complexity is lower than models constructed without feature selection is that the number of features, which is the most important parameter in time complexity, is low. The statistical analysis was performed on the pre-processed data and meaningful information was produced from the data using machine learning algorithms.
引用
收藏
页数:15
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