Predicting recurring concepts on data-streams by me ans of a meta-model and a fuzzy similarity function

Abad Arranz, Miguel Ángel and Gomes, João Bartolo and Menasalvas Ruiz, Ernestina (2015). Predicting recurring concepts on data-streams by me ans of a meta-model and a fuzzy similarity function. "Expert Systems With Applications", v. 46 ; pp. 87-105. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2015.10.022.

Description

Title: Predicting recurring concepts on data-streams by me ans of a meta-model and a fuzzy similarity function
Author/s:
  • Abad Arranz, Miguel Ángel
  • Gomes, João Bartolo
  • Menasalvas Ruiz, Ernestina
Item Type: Article
Título de Revista/Publicación: Expert Systems With Applications
Date: March 2015
Volume: 46
Subjects:
Freetext Keywords: Data stream mining; Hidden Markov model; Fuzzy logic; Concept drift; Classification
Faculty: E.T.S. de Ingenieros Informáticos (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

Full text

[img]
Preview
PDF - Requires a PDF viewer, such as GSview, Xpdf or Adobe Acrobat Reader
Download (1MB) | Preview

Abstract

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.

More information

Item ID: 43634
DC Identifier: http://oa.upm.es/43634/
OAI Identifier: oai:oa.upm.es:43634
DOI: 10.1016/j.eswa.2015.10.022
Official URL: http://www.sciencedirect.com/science/article/pii/S0957417415007174
Deposited by: Memoria Investigacion
Deposited on: 24 Mar 2017 12:45
Last Modified: 24 Mar 2017 13:23
  • Logo InvestigaM (UPM)
  • Logo GEOUP4
  • Logo Open Access
  • Open Access
  • Logo Sherpa/Romeo
    Check whether the anglo-saxon journal in which you have published an article allows you to also publish it under open access.
  • Logo Dulcinea
    Check whether the spanish journal in which you have published an article allows you to also publish it under open access.
  • Logo de Recolecta
  • Logo del Observatorio I+D+i UPM
  • Logo de OpenCourseWare UPM