Mining recurring concepts in a dynamic feature space

Bártolo Gomes, Joao Paulo and Medhat Gaber, Mohamed and Sousa, Pedro and Menasalvas Ruiz, Ernestina (2013). Mining recurring concepts in a dynamic feature space. "IEEE Transactions on Neural Networks and Learning Systems", v. PP (n. 99); pp. 1-16. ISSN 2162-237X. https://doi.org/10.1109/TNNLS.2013.2271915.

Description

Title: Mining recurring concepts in a dynamic feature space
Author/s:
  • Bártolo Gomes, Joao Paulo
  • Medhat Gaber, Mohamed
  • Sousa, Pedro
  • Menasalvas Ruiz, Ernestina
Item Type: Article
Título de Revista/Publicación: IEEE Transactions on Neural Networks and Learning Systems
Date: July 2013
ISSN: 2162-237X
Volume: PP
Subjects:
Freetext Keywords: Concept drift, data stream mining, dynamic feature space (DFS), recurring concepts, concepto deribado, extracción de datos, espacio de carácter dinámico, conceptos recurrentes
Faculty: Facultad de Informática (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

Most data stream classification techniques assume that the underlying feature space is static. However, in real-world applications the set of features and their relevance to the target concept may change over time. In addition, when the underlying concepts reappear, reusing previously learnt models can enhance the learning process in terms of accuracy and processing time at the expense of manageable memory consumption. In this paper, we propose mining recurring concepts in a dynamic feature space (MReC-DFS), a data stream classification system to address the challenges of learning recurring concepts in a dynamic feature space while simultaneously reducing the memory cost associated with storing past models. MReC-DFS is able to detect and adapt to concept changes using the performance of the learning process and contextual information. To handle recurring concepts, stored models are combined in a dynamically weighted ensemble. Incremental feature selection is performed to reduce the combined feature space. This contribution allows MReC-DFS to store only the features most relevant to the learnt concepts, which in turn increases the memory efficiency of the technique. In addition, an incremental feature selection method is proposed that dynamically determines the threshold between relevant and irrelevant features. Experimental results demonstrating the high accuracy of MReC-DFS compared with state-of-the-art techniques on a variety of real datasets are presented. The results also show the superior memory efficiency of MReC-DFS.

More information

Item ID: 20453
DC Identifier: http://oa.upm.es/20453/
OAI Identifier: oai:oa.upm.es:20453
DOI: 10.1109/TNNLS.2013.2271915
Official URL: http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=6572821&tag=1
Deposited by: Biblioteca Facultad de Informatica
Deposited on: 23 Sep 2013 14:52
Last Modified: 21 Apr 2016 23:16
  • 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