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Paper details
Number 1 - March 2023
Volume 33 - 2023
A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm
Santhosam Kavi Priya, Kasirajan Pon Karthika
Abstract
Depression is one of the primary causes of global mental illnesses and an underlying reason for suicide. The user generated
text content available in social media forums offers an opportunity to build automatic and reliable depression detection
models. The core objective of this work is to select an optimal set of features that may help in classifying depressive contents
posted on social media. To this end, a novel multi-objective feature selection technique (EFS-pBGSK) and machine learning
algorithms are employed to train the proposed model. The novel feature selection technique incorporates a binary gaining-sharing knowledge-based optimization algorithm with population reduction (pBGSK) to obtain the optimized features from
the original feature space. The extensive feature selector (EFS) is used to filter out the excessive features based on their
ranking. Two text depression datasets collected from Twitter and Reddit forums are used for the evaluation of the proposed
feature selection model. The experimentation is carried out using naive Bayes (NB) and support vector machine (SVM)
classifiers for five different feature subset sizes (10, 50, 100, 300 and 500). The experimental outcome indicates that the
proposed model can achieve superior performance scores. The top results are obtained using the SVM classifier for the
SDD dataset with 0.962 accuracy, 0.929 F1 score, 0.0809 log-loss and 0.0717 mean absolute error (MAE). As a result,
the optimal combination of features selected by the proposed hybrid model significantly improves the performance of the
depression detection system.
Keywords
depression detection, text classification, dimensionality reduction, hybrid feature selection, binary gaining-sharing knowledge-based optimization