Szczegóły publikacji
Opis bibliograficzny
Comparison of manual and automated feature engineering for daily activity classification in mental disorder diagnosis / Jakub Adamczyk, Filip MALAWSKI // Computing and Informatics / Slovak Academy of Sciences. Institute of Informatics ; ISSN 1335-9150. — Tytuł poprz.: Computers and Artificial Intelligence. — 2021 — vol. 40 no. 4, s. 850–879. — Bibliogr. s. 875–879, Abstr. — Publikacja dostępna online od: 2021-12-14. — XXII KKIO Software Engineering Conference : Krakow on 21–22 September 2021
Autorzy (2)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 139148 |
|---|---|
| Data dodania do BaDAP | 2022-02-22 |
| Tekst źródłowy | URL |
| DOI | 10.31577/cai_2021_4_850 |
| Rok publikacji | 2021 |
| Typ publikacji | referat w czasopiśmie |
| Otwarty dostęp |  |
| Creative Commons | |
| Czasopismo/seria | Computing and Informatics |
Abstract
Motor activity data allows for analysis of complex behavioral patterns, including the diagnosis of mental disorders, such as depression or schizophrenia. However, the classification of actigraphy signals remains a challenge. The main reasons are small datasets and the need for sophisticated feature engineering. The recent development of AutoML approaches allows for automating feature extraction and selection. In this work, we compare automatic and manual feature engineering for applications in mental health. We also analyze classifier evaluation methods for small datasets. The automated approach results in better classification, as measured with several metrics, and in a shorter, cleaner code, providing software engineering advantages.