A contemporary multi-objective feature selection model for depression detection using a hybrid pBGSK optimization algorithm

• Autorzy: Kavi Priya Santhosam , Pon Karthika Kasirajan

Identyfikatory

DOI

10.34768/amcs-2023-0010

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

depression detection; text classification; dimensionality reduction; hybrid feature selection

PL

wykrywanie depresji; klasyfikacja tekstu; redukcja wymiarowości; wybór funkcji

• Wydawca: Oficyna Wydawnicza Uniwersytetu Zielonogórskiego
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2023
• Tom: Vol. 33, no. 1
• Strony: 117--131
• Opis fizyczny: Bibliogr. 44 poz., rys., tab., wykr.

Twórcy

autor

Kavi Priya Santhosam

• Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, 626005, Tamil Nadu, India

autor

Pon Karthika Kasirajan

• Department of Computer Science and Engineering, Mepco Schlenk Engineering College (Autonomous), Sivakasi, 626005, Tamil Nadu, India

Bibliografia

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Uwagi

PL

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023)