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A fast nearest neighbor classifier based on self-organizing incremental neural network (SOINN)

A fast nearest neighbor classifier based on self-organizing incremental neural network (SOINN). Presenter : Lin, Shu -Han Authors : Shen Furao ,, Osamu Hasegawa. Neuron Networks (NN, 2008). Outline. Introduction Motivation Objective Methodology Experiments Conclusion Comments.

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A fast nearest neighbor classifier based on self-organizing incremental neural network (SOINN)

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  1. A fast nearest neighbor classifier based onself-organizing incremental neural network(SOINN) Presenter : Lin, Shu-Han • Authors : ShenFurao,, Osamu Hasegawa Neuron Networks (NN, 2008)

  2. Outline • Introduction • Motivation • Objective • Methodology • Experiments • Conclusion • Comments

  3. Introduction-self-organizing incremental neural network(SOINN) Distance: Toofar Node =prototype

  4. Introduction-self-organizing incremental neural network(SOINN) Link age

  5. Introduction-self-organizing incremental neural network(SOINN) Age: Tooold

  6. Introduction-self-organizing incremental neural network(SOINN) Insertnodeiferrorislarge CancelInsertionifinsertisnouse Runtwotimes

  7. Introduction-self-organizing incremental neural network(SOINN) Deleteoutlier: Nodeswithoutneighbor(low-densityassumption) Runtwotimes

  8. Motivation • SOINNclassifier(theirfirstresearchin2005) • Use6userdeterminedparameters • Donotmentionedaboutnoise • Toomanyprototypes • Unsupervisedlearning • Theirsecondresearch(in2007)talkabouttheseweakness

  9. Objectives • Propose a ImprovedversionofSOINN,ASC(AdjustSOINNClassifier) • FASTER: delete/less prototype • Training phase • Classification phase • CLASSIFIER: 1-NN (prototype) rule • INCREMENTAL LEARNING • ONE LAYER: easy to understand the setting, less parameters~ • MORE STABLE: help of k-means

  10. Methodology– Adjusted SOINN Distance: Toofar Anodeisacluster 10

  11. Methodology– Adjusted SOINN Link age 11

  12. Methodology– Adjusted SOINN Winner Neighbor 12

  13. Methodology– Adjusted SOINN Age: Too old > ad 13

  14. Methodology– Adjusted SOINN Deleteoutlier: Nodeswithoutneighbor(low-densityassumption) 14

  15. Methodology– Adjusted SOINN Lambda = iterations 15

  16. Methodology– k-means • Help of k-means clustering, k = # of neurons • Adjust the result prototypes: assume that each node nearby the centroid of class 16

  17. Methodology– noise-reduction • Help of k-Edit Neighbors Classifier (ENC), k=? • Delete the node which label are differs from the majority voting of its k-neighbors: assume that are generated by noise 17

  18. Methodology– center-cleaning Delete neurons: if it has never been the nearest neuron to other class: assume that are lies in the central part of class 18

  19. Experiments: Artificial dataset dataset Adjusted SOINN Error: same Speed: faster 19 ASC

  20. Experiments: Artificial dataset dataset Adjusted SOINN Error: same Speed: faster 20 ASC

  21. Experiments: Artificial dataset dataset Adjusted SOINN Error: better Speed: faster 21 ASC

  22. Experiments: Artificial dataset dataset Adjusted SOINN Error: better Speed: faster 22 ASC

  23. Experiments: Real dataset Compression ratio (%) Speed up ratio (%) 23

  24. Experiments: Compare with other prototype-based classification method Nearest Subclass Classifier (NSC) k-Means Classifier (KMC) k-NN Classifier (NNC) Learning Vector Quantization (LVQ) 24

  25. Experiments: Compare with other prototype-based classification method 25

  26. Conclusions • ASC • Learns the number of nodes needed to determine the decision boundary • Incremental neural network • Robust to noisy training data • Fast classification • Fewer parameters: 3 parameters

  27. Comments • Advantage • Improve many things • A previous paper to demonstrate the thing they want to modify • Drawback • NO Suggestion of parameters • Application • A work from unsupervised learning to supervised learning

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