Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/106864
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical and Electronic Engineering | en_US |
dc.creator | Ke, X | en_US |
dc.creator | Mak, MW | en_US |
dc.creator | Meng, HM | en_US |
dc.date.accessioned | 2024-06-06T06:06:04Z | - |
dc.date.available | 2024-06-06T06:06:04Z | - |
dc.identifier.issn | 0893-6080 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/106864 | - |
dc.language.iso | en | en_US |
dc.publisher | Elsevier Ltd | en_US |
dc.subject | Dementia detection | en_US |
dc.subject | Feature ranking | en_US |
dc.subject | Feature selection | en_US |
dc.subject | Spoken language biomarkers | en_US |
dc.title | Automatic selection of spoken language biomarkers for dementia detection | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 191 | en_US |
dc.identifier.epage | 204 | en_US |
dc.identifier.volume | 169 | en_US |
dc.identifier.doi | 10.1016/j.neunet.2023.10.018 | en_US |
dcterms.abstract | This paper analyzes diverse features extracted from spoken language to select the most discriminative ones for dementia detection. We present a two-step feature selection (FS) approach: Step 1 utilizes filter methods to pre-screen features, and Step 2 uses a novel feature ranking (FR) method, referred to as dual dropout ranking (DDR), to rank the screened features and select spoken language biomarkers. The proposed DDR is based on a dual-net architecture that separates FS and dementia detection into two neural networks (namely, the operator and selector). The operator is trained on features obtained from the selector to reduce classification or regression loss. The selector is optimized to predict the operator’s performance based on automatic regularization. Results show that the approach significantly reduces feature dimensionality while identifying small feature subsets that achieve comparable or superior performance compared with the full, default feature set. The Python codes are available at https://github.com/kexquan/dual-dropout-ranking. | en_US |
dcterms.accessRights | embargoed access | en_US |
dcterms.bibliographicCitation | Neural networks, Jan. 2024, v. 169, p. 191-204 | en_US |
dcterms.isPartOf | Neural networks | en_US |
dcterms.issued | 2024-01 | - |
dc.identifier.scopus | 2-s2.0-85174961228 | - |
dc.identifier.eissn | 1879-2782 | en_US |
dc.description.validate | 202406 bcch | en_US |
dc.description.oa | Not applicable | en_US |
dc.identifier.FolderNumber | a2778 | - |
dc.identifier.SubFormID | 48313 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.date.embargo | 2026-01-31 | en_US |
dc.description.oaCategory | Green (AAM) | en_US |
Appears in Collections: | Journal/Magazine Article |
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