Collaborative data stream mining in ubiquitous environments using dynamic classifier selection

Bártolo Gomes, Joao Paulo and Medhat Gaber, Mohamed and Sousa, Pedro and Menasalvas Ruiz, Ernestina (2013). Collaborative data stream mining in ubiquitous environments using dynamic classifier selection. "International Journal of Information Technology & Decision Making", v. 12 ; pp.. ISSN 0219-6220.

Description

Title: Collaborative data stream mining in ubiquitous environments using dynamic classifier selection
Author/s:
  • Bártolo Gomes, Joao Paulo
  • Medhat Gaber, Mohamed
  • Sousa, Pedro
  • Menasalvas Ruiz, Ernestina
Item Type: Article
Título de Revista/Publicación: International Journal of Information Technology & Decision Making
Date: 2013
ISSN: 0219-6220
Volume: 12
Subjects:
Freetext Keywords: Collaborative Data Stream Mining, Ubiquitous Knowledge Discovery, Performance evaluation, Descubrimiento de conocimiento ubicuo, Búsqueda de datos por flujos colaborativos, Evaluación de la presentación.
Faculty: Facultad de Informática (UPM)
Department: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Creative Commons Licenses: Recognition - No derivative works - Non commercial

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Abstract

In ubiquitous data stream mining applications, different devices often aim to learn concepts that are similar to some extent. In these applications, such as spam filtering or news recommendation, the data stream underlying concept (e.g., interesting mail/news) is likely to change over time. Therefore, the resultant model must be continuously adapted to such changes. This paper presents a novel Collaborative Data Stream Mining (Coll-Stream) approach that explores the similarities in the knowledge available from other devices to improve local classification accuracy. Coll-Stream integrates the community knowledge using an ensemble method where the classifiers are selected and weighted based on their local accuracy for different partitions of the feature space. We evaluate Coll-Stream classification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show that Coll-Stream resultant model achieves stability and accuracy in a variety of situations using both synthetic and real world datasets.

More information

Item ID: 19176
DC Identifier: http://oa.upm.es/19176/
OAI Identifier: oai:oa.upm.es:19176
Official URL: http://www.worldscientific.com/worldscinet/ijitdm
Deposited by: Memoria Investigacion
Deposited on: 23 Sep 2013 13:38
Last Modified: 21 Apr 2016 17:24
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