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A Nearest Neighbor Classifier for High-Speed Big Data Streams with Instance Selection (spark-IS-streaming)

Here we present an efficient nearest neighbor solution to classify fast and massive data streams using Apache Spark. It is formed by a distributed case-base and an instance selection method that enhances its performance and effectiveness. A distributed metric tree (based on M-trees) has been designed to organize the case-base and consequently to speed up the neighbor searches. This distributed tree consists of a top-tree (in the master node) that routes the searches in the first levels and several leaf nodes (in the slaves nodes) that solve the searches in next levels through a completely parallel scheme.

A improved local version of RNGE [1] in order to control the insertion and removal of noisy instances. For each incoming example, a relative graph is built around each new instance and its subset of neighbors. The local graphs are then used to edit the case-base by deciding what instances should be inserted, removed or left intact.

Associated Spark package:

Associated journal paper: S. Ramírez-Gallego, B. Krawczyk, S. García, M. Woźniak, J. M. Benítez and F. Herrera, "Nearest Neighbor Classification for High-Speed Big Data Streams Using Spark," in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 47, no. 10, pp. 2727-2739, Oct. 2017. doi: 10.1109/TSMC.2017.2700889 URL:


Our distributed approach includes several user-defined input parameters, which are described below:

  • "type": type of file. "keel" for KEEL/ARFF files, "labeled" for Spark generated files with labeled points, or "csv" for standard csv files. Default: "csv".
  • "header": path of header file (only valid for KEEL files).
  • "output": path of output directory.
  • "npart": number of partitions for the first partitioning process. Default: 4.
  • "k": number of neighbors selected for prediction. Default: 1.
  • "kGraph": number of neighors selected to construct local graphs. Default: 10.
  • "rate": number of examples in each batch. Default: 2500.
  • "interval": the time interval at which streaming data will be divided into batches. Default: 1000 (ms).
  • "seed": seed value for random generator. Default: 237597430.
  • "ntrees": number of sub-trees in the slave nodes. It should be set equal or higher than the number of initial partitions. Default: 4.
  • "overlap": the distance between elements in sub-trees. 0 means sub-trees are disjoint. Default: 0.
  • "edited": if filtering of new noisy examples ocurrs. Default: false.
  • "removedOld": if removal of already inserted examples considered as noise ocurrs. Default: false.
  • "timeout": milleseconds before shutting the streaming execution. Default: 600000.
  • "sampling": percent of sampling w/o replacement on original static data. Default: 0.0.
  • "nClass": number of classes in data. Default: 2 (binary).


spark-submit --class org.ugr.sci2s.mllib.test.QueuRDDStreamingTest spark-IS-streaming.jar --input=hdfs://localhost:8020/ --output=hdfs://localhost:8020/output/streaming-test --type=csv --interval=1000 --rate=100000 --ntrees=460 --npart=460 --edited=true

For a more thorough sourc code example, please refer to: src/main/scala/org/ugr/sci2s/mllib/test/QueuRDDStreamingTest.scala



[1] J.S. Sánchez, F. Pla, F.J. Ferri, Prototype selection for the nearest neighbour rule through proximity graphs, Pattern Recognition Lett. 18 (1997) 507–513. KEEL project: /


A Nearest Neighbor Classifier for High-Speed Big Data Streams with Instance Selection




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