Classification algorithms for big data analysis, a map reduce approach

V. A. Ayma, R. S. Ferreira, P. Happ, D. Oliveira, R. Feitosa, G. Costa, A. Plaza, P. Gamba

Producción científica: Contribución a una revistaArtículo de la conferenciarevisión exhaustiva

40 Citas (Scopus)


Since many years ago, the scientific community is concerned about how to increase the accuracy of different classification methods, and major achievements have been made so far. Besides this issue, the increasing amount of data that is being generated every day by remote sensors raises more challenges to be overcome. In this work, a tool within the scope of InterIMAGE Cloud Platform (ICP), which is an open-source, distributed framework for automatic image interpretation, is presented. The tool, named ICP: Data Mining Package, is able to perform supervised classification procedures on huge amounts of data, usually referred as big data, on a distributed infrastructure using Hadoop MapReduce. The tool has four classification algorithms implemented, taken from WEKA's machine learning library, namely: Decision Trees, Naïve Bayes, Random Forest and Support Vector Machines (SVM). The results of an experimental analysis using a SVM classifier on data sets of different sizes for different cluster configurations demonstrates the potential of the tool, as well as aspects that affect its performance.

Idioma originalInglés
Páginas (desde-hasta)17-21
Número de páginas5
PublicaciónInternational Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives
EstadoPublicada - 2015
Publicado de forma externa
EventoJoint ISPRS Conference on Photogrammetric Image Analysis, PIA 2015 and High Resolution Earth Imaging for Geospatial Information, HRIGI 2015 - Munich, Alemania
Duración: 25 mar. 201527 mar. 2015


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