COLLABORATIVE DATA STREAM MINING IN UBIQUITOUS ENVIRONMENTS USING DYNAMIC CLASSIFIER SELECTION

João Bártolo Gomes (), Mohamed Medhat Gaber (), Pedro A. C. Sousa () and Ernestina Menasalvas ()
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João Bártolo Gomes: Institute for Infocomm Research (I2R), A*STAR, Singapore, 1 Fusionopolis Way Connexis, Singapore 138632, Singapore
Mohamed Medhat Gaber: School of Computing Science and Digital Media, Robert Gordon University, Riverside East, Garthdee Road, Aberdeen, AB10 7GJ, UK
Pedro A. C. Sousa: Faculdade de Ciências e Tecnologia, Universidade Nova de Lisboa, Quinta da Torre, 2825-114, Caparica, Portugal
Ernestina Menasalvas: Facultad de Informática, Universidad Politécnica de Madrid, Campus de Montegancedo, s/n 28660 Boadilla del Monte, Madrid, Spain

International Journal of Information Technology & Decision Making (IJITDM), 2013, vol. 12, issue 06, 1287-1308

Abstract: In ubiquitous data stream mining, different devices often aim to learn concepts that are similar to some extent. In many 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-Streamintegrates 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 evaluateColl-Streamclassification accuracy in situations with concept drift, noise, partition granularity and concept similarity in relation to the local underlying concept. The experimental results show thatColl-Streamresultant model achieves stability and accuracy in a variety of situations using both synthetic and real-world datasets.

Keywords: Collaborative data stream mining; ubiquitous knowledge discovery; concept drift; performance evaluation (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1142/S0219622013500375

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