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• W3093483861 abstract "Like many psychological scales, depression scales are ordinal in nature. Depression prediction from behavioral signals has so far been posed either as classification or regression problems. However, these naive approaches have fundamental issues because they are not focused on ranking, unlike ordinal regression, which is the most appropriate approach. Ordinal regression to date has comparatively few methods when compared with other branches in machine learning, and its usage is limited to specific research domains. Ordinal logistic regression (OLR) is one such method, which is an extension for ordinal data of the well-known logistic regression, but is not familiar in speech processing, affective computing or depression prediction. The primary aim of this article is to investigate proportionality structures and model selection for the design of ordinal regression systems within the logistic regression framework. A new greedy-based algorithm for partial proportional odds model selection (GREP) is proposed that allows the parsimonious design of effective ordinal logistic regression models, which avoids an exhaustive search and outperforms model selection using the Brant test. Evaluations on the DAIC-WOZ and AViD depression corpora show that OLR models exploiting GREP can outperform two competitive baseline systems (GSR and CNN), in terms of both RMSE and Spearman correlation." @default.
• W3093483861 created "2020-10-29" @default.
• W3093483861 creator A5021883844 @default.
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• W3093483861 date "2023-01-01" @default.
• W3093483861 modified "2023-09-25" @default.
• W3093483861 title "Ordinal Logistic Regression With Partial Proportional Odds for Depression Prediction" @default.
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• W3093483861 doi "https://doi.org/10.1109/taffc.2020.3031300" @default.
• W3093483861 hasPublicationYear "2023" @default.
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