DOIONLINE 
DOIONLINE Publish In |
International Journal of Advances in Electronics and Computer Science-IJAECS |
 Journal Home Volume Issue |
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Issue |
Volume-10,Issue-1 ( Jan, 2023 ) | |||||||||
Paper Title |
Feature Engineering in the Context of Classification Involving Dual Mood Music | |||||||||
Author Name |
Carlisle Dominic Lorenzo, Andrei D. Coronel | |||||||||
Affilition |
1Dept. of Information Systems and Computer Science, Ateneo De Manila University 2Dept. of Information Systems and Computer Science, Ateneo De Manila University | |||||||||
Pages |
44-50 | |||||||||
Abstract |
Abstract -This paper compares feature set variations and several methods of machine learning techniques for the classification of music that may be labeled under two mood categories which will henceforth be called dual mood music. The study tests a dataset of dual mood music which was created by gathering annotations of mood from the AllMusic website. This entails the building of feature sets using three different ways: a feature set based on music principles, a feature set built using Kendall’s Tau correlation on music extracted by the jSymbolic software, and feature set built by using a decision tree as a feature selection tool prior to classification. These feature sets were then tested using SVM, Naive Bayes and decision tree algorithms, using accuracy and F-measure as metrics of performance. The best feature set in these experiments resulted with a classification accuracy of 82.0% in the initial splitting for data points and an F-measure of 81.3%, using the decision tree when classifying songs with low valence. | |||||||||
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