The classification of imbalanced large data sets based on MapReduce and ensemble of ELM classifiers

  • Junhai ZhaiEmail author
  • Sufang Zhang
  • Chenxi Wang
Original Article


Aiming at effectively classifying imbalanced large data sets with two classes, this paper proposed a novel algorithm, which consists of four stages: (1) alternately over-sample p times between positive class instances and negative class instances; (2) construct l balanced data subsets based on the generated positive class instances; (3) train l component classifiers with extreme learning machine (ELM) algorithm on the constructed l balanced data subsets; (4) integrate the l ELM classifiers with simple voting approach. Specifically, in first stage, we firstly calculate the center of positive class instances, and then sample instance points along the line between the center and each positive class instance. Next, for each instance point in the new positive class, we firstly find its k nearest neighbors in negative class instances with MapRedcue, and then sample instance points along the line between the instance and its k nearest negative neighbors. The process of over-sampling is repeated p times. In the second stage, we sample instances l times from the negative class with the same size as the generated positive class instances. Each round of sampling, we put positive class and negative class instances together thus obtain l balanced data subsets. The experimental results show that the proposed algorithm can obtain promising speed-up and scalability, and also outperforms three other ensemble algorithms in G-mean.


Imbalanced large data sets MapReduce Extreme learning machine Ensemble learning Majority voting method 



This research is supported by the national natural science foundation of China (61170040, 71371063), by the natural science foundation of Hebei Province (F2013201110, F2013201220), by the Key Scientific Research Foundation of Education Department of Hebei Province (ZD20131028), and by the Opening Fund of Zhejiang Provincial Top Key Discipline of Computer Science and Technology at Zhejiang Normal University, China.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.Key Laboratory of Machine Learning and Computational Intelligence, College of Mathematics and Information ScienceHebei UniversityBaodingChina
  2. 2.College of Mathematics, Physics and Information EngineeringZhejiang Normal UniversityJinhuaChina
  3. 3.Hebei Branch of Meteorological Cadres Training InstituteChina Meteorological AdministrationBaodingChina
  4. 4.College of Computer Science and TechnologyHebei UniversityBaodingChina

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