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Memorias de investigación
Artículos en revistas:
Predicting recurring concepts on data-streams by me ans of a meta-model and a fuzzy similarity function
Año:2015
Áreas de investigación
  • Inteligencia artificial
Datos
Descripción
Meta-models can be used in the process of enhancing the drift detection mechanisms used by data stream algorithms, by representing and predicting when the change will occur. There are some real-world situations where a concept reappears, as in the case of intrusion detection systems(IDS), where the same incidents or an adaptation of them usually reappear over time. In these environments the early prediction of drift by means of a better knowledge of past models can help to anticipate to the change, thus improving efficiency of the model regarding the training instances needed. In this paper we present MM-PRec, a meta-model for predicting recurring concepts on data-streams which main goal is to predict when the drift is going to occur together with the best model to be used in case of a recurring concept. To fulfill this goal, MM-PRec trains a Hidden Markov Model (HMM) from the instances that appear during the concept drift. The learning process of the base classification learner feeds the meta-model with all the information needed to predict recurrent or similar situations. Thus, the models predicted together with the associated contextual information are stored. In our approach we also propose to use a fuzzy similarity function to decide which is the best model to represent a particular context when drift is detected. The experiments performed show that MM-PRec outperforms the behaviour of other context-aware algorithms in terms of training instances needed, specially in environments characterized by the presence of gradual drifts.
Internacional
Si
JCR del ISI
Si
Título de la revista
Expert Systems With Applications
ISSN
0957-4174
Factor de impacto JCR
2,24
Información de impacto
Datos JCR del año 2014
Volumen
46
DOI
10.1016/j.eswa.2015.10.022
Número de revista
Desde la página
87
Hasta la página
105
Mes
MARZO
Ranking
Esta actividad pertenece a memorias de investigación
Participantes
  • Autor: Miguel Angel Abad Arranz (UPM)
  • Autor: Joao Bartolo Gomes (UPM)
  • Autor: Ernestina Menasalvas Ruiz (UPM)
Grupos de investigación, Departamentos, Centros e Institutos de I+D+i relacionados
  • Creador: Centro o Instituto I+D+i: Centro de tecnología Biomédica CTB
  • Departamento: Lenguajes y Sistemas Informáticos e Ingeniería de Software
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