Artigo De: cienciavitae, dblp, scopus, orcid

Multi-period classification: learning sequent classes from temporal domains

Data Mining and Knowledge Discovery

Henriques, R.; Madeira, S.C.; Antunes, C. — 2015 — Springer

Informações chave

Autores:

Henriques, R. (Rui Miguel Carrasqueiro Henriques); Madeira, S.C. (Sara Alexandra Cordeiro Madeira); Antunes, C. (Claudia Martins Antunes)

Publicado em

April 10, 2015

Resumo

As the majority of real-world decisions change over time, extending traditional classifiers to deal with the problem of classifying an attribute of interest across different time periods becomes increasingly important. Tackling this problem, referred to as multi-period classification, is critical to answer real-world tasks, such as the prediction of upcoming healthcare needs or administrative planning tasks. In this context, although existing research provides principles for learning single labels from complex data domains, less attention has been given to the problem of learning sequences of classes (symbolic time series). This work motivates the need for multi-period classifiers, and proposes a method, cluster-based multi-period classification (CMPC), that preserves local dependencies across the periods under classification. Evaluation against real-world datasets provides evidence of the relevance of multi-period classifiers, and shows the superior performance of the CMPC method against peer methods adapted from long-term prediction for multi-period tasks with a high number of periods.

Detalhes da publicação

Autores da comunidade :

Versão da publicação

P - Versão editada

Editora

Springer

Ligação para a versão da editora

https://link.springer.com/article/10.1007/s10618-014-0376-8

Título do contentor da publicação

Data Mining and Knowledge Discovery

Primeira página ou número de artigo

1

Última página

28

Volume

29

Fascículo

3

ISSN

1384-5810

DOI

10.1007/s10618-014-0376-8

Domínio Científico (FOS)

computer-and-information-sciences - Ciências da Computação e da Informação

Idioma da publicação (código ISO)

eng - Inglês

Identificador alternativo (URI)

http://www.scopus.com/inward/record.url?eid=2-s2.0-85028229472&partnerID=MN8TOARS

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Acesso apenas a metadados