Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/163180
Title: Ensemble hybrid learning methods for automated depression detection
Authors: Ansari, Luna
Ji, Shaoxiong
Chen, Qian
Cambria, Erik
Keywords: Engineering::Computer science and engineering
Issue Date: 2022
Source: Ansari, L., Ji, S., Chen, Q. & Cambria, E. (2022). Ensemble hybrid learning methods for automated depression detection. IEEE Transactions On Computational Social Systems, 1-9. https://dx.doi.org/10.1109/TCSS.2022.3154442
Journal: IEEE Transactions on Computational Social Systems
Abstract: Changes in human lifestyle have led to an increase in the number of people suffering from depression over the past century. Although in recent years, rates of diagnosing mental illness have improved, many cases remain undetected. Automated detection methods can help identify depressed or individuals at risk. An understanding of depression detection requires effective feature representation and analysis of language use. In this article, text classifiers are trained for depression detection. The key objective is to improve depression detection performance by examining and comparing two sets of methods: hybrid and ensemble. The results show that ensemble models outperform the hybrid model classification results. The strength and effectiveness of the combined features demonstrate that better performance can be achieved by multiple feature combinations and proper feature selection.
URI: https://hdl.handle.net/10356/163180
ISSN: 2329-924X
DOI: 10.1109/TCSS.2022.3154442
Rights: © 2022 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Journal Articles

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