The Extended Dawid-Skene Model: Fusing Information from Multiple Data Schemas

Autor:	Camilleri, Michael P. J., Williams, Christopher K. I.
Rok vydání:	2019
Předmět:	Statistics - Machine Learning Computer Science - Human-Computer Interaction Computer Science - Machine Learning
Zdroj:	in ECML PKDD 2019 Workshops, CCIS 1167, pp. 121 - 136, 2020
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-030-43823-4_11
Popis:	While label fusion from multiple noisy annotations is a well understood concept in data wrangling (tackled for example by the Dawid-Skene (DS) model), we consider the extended problem of carrying out learning when the labels themselves are not consistently annotated with the same schema. We show that even if annotators use disparate, albeit related, label-sets, we can still draw inferences for the underlying full label-set. We propose the Inter-Schema AdapteR (ISAR) to translate the fully-specified label-set to the one used by each annotator, enabling learning under such heterogeneous schemas, without the need to re-annotate the data. We apply our method to a mouse behavioural dataset, achieving significant gains (compared with DS) in out-of-sample log-likelihood (-3.40 to -2.39) and F1-score (0.785 to 0.864). Comment: Updated with Author-Preprint version following Publication in P. Cellier and K. Driessens (Eds.): ECML PKDD 2019 Workshops, CCIS 1167, pp. 121 - 136, 2020
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1906.01251 Zobrazit plný text záznamu View this record from Arxiv