A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm - Kavi Priya, Santhosam; Pon Karthika, Kasirajan (Zielona Góra: Uniwersytet Zielonogórski)
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.
Keywords: depression detection; text classification; dimensionality reduction; hybrid feature selection; binary gaining-sharing knowledge-based optimization