Mining recurring concepts in a dynamic feature space

Bártolo Gomes, Joao Paulo; Medhat Gaber, Mohamed; Sousa, Pedro y 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.

Descripción

Título: Mining recurring concepts in a dynamic feature space
Autor/es:
  • Bártolo Gomes, Joao Paulo
  • Medhat Gaber, Mohamed
  • Sousa, Pedro
  • Menasalvas Ruiz, Ernestina
Tipo de Documento: Artículo
Título de Revista/Publicación: IEEE Transactions on Neural Networks and Learning Systems
Fecha: Julio 2013
Volumen: PP
Materias:
Palabras Clave Informales: Concept drift, data stream mining, dynamic feature space (DFS), recurring concepts, concepto deribado, extracción de datos, espacio de carácter dinámico, conceptos recurrentes
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Lenguajes y Sistemas Informáticos e Ingeniería del Software
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

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.

Más información

ID de Registro: 20453
Identificador DC: http://oa.upm.es/20453/
Identificador OAI: oai:oa.upm.es:20453
Depositado por: Biblioteca Facultad de Informatica
Depositado el: 23 Sep 2013 14:52
Ultima Modificación: 21 Abr 2016 23:16
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