Forward feature selection for toxic speech classification using support vector machine and random forest

This study describes the methods for eliminating irrelevant features in
speech data to enhance toxic speech classification accuracy and reduce the
complexity of the learning process. Therefore, the wrapper method is
introduced to estimate the forward selection technique based on support
vector machine (SVM) and random forest (RF) classifier algorithms. Eight
main speech features were then extracted with derivatives consisting of 9
statistical sub-features from 72 features in the extraction process.
Furthermore, Python is used to implement the classifier algorithm of 2,000
toxic data collected through the worlds largest video sharing media, known
as YouTube. Conclusively, this experiment shows that after the feature
selection process, the classification performance using SVM and RF
algorithms increases to an excellent extent. We were able to select 10 speech
features out of 72 original feature sets using the forward feature selection
method, with 99.5% classification accuracy using RF and 99.2% using
SVM.