Intelligent Database Systems Lab N.Y.U.S.T. I. M. A fast nearest neighbor classifier based on self-organizing incremental neural network (SOINN) Neuron.

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Intelligent Database Systems Lab N.Y.U.S.T. I. M. A fast nearest neighbor classifier based on self-organizing incremental neural network (SOINN) Neuron Networks (NN, 2008) Presenter : Lin, Shu-Han Authors : Shen Furao,, Osamu Hasegawa

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 2 Outline Introduction Motivation Objective Methodology Experiments Conclusion Comments

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 3 Introduction - self-organizing incremental neural network (SOINN) Distance: Too far Node = prototype

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 4 Introduction - self-organizing incremental neural network (SOINN) Link age

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 5 Introduction - self-organizing incremental neural network (SOINN) Age: Too old

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 6 Introduction - self-organizing incremental neural network (SOINN) Run two times Insert node if error is large Cancel Insertion if insert is no use

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 7 Introduction - self-organizing incremental neural network (SOINN) Run two times Delete outlier: Nodes without neighbor (low-density assumption)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Motivation SOINN classifier (their first research in 2005)  Use 6 user determined parameters  Do not mentioned about noise  Too many prototypes  Unsupervised learning Their second research (in 2007) talk about these weakness 8

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Objectives Propose a Improved version of SOINN, ASC (Adjust SOINN Classifier)  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 9

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Adjusted SOINN 10 Distance: Too far A node is a cluster

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Adjusted SOINN 11 Link age

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Adjusted SOINN 12 Winner Neighbor

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Adjusted SOINN 13 Age: Too old > a d

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Adjusted SOINN 14 Delete outlier: Nodes without neighbor (low-density assumption)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – Adjusted SOINN 15 Lambda = iterations

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – k-means 16 Help of k-means clustering, k = # of neurons  Adjust the result prototypes: assume that each node nearby the centroid of class

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – noise-reduction 17 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

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Methodology – center-cleaning 18 Delete neurons: if it has never been the nearest neuron to other class: assume that are lies in the central part of class

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments: Artificial dataset 19 dataset Adjusted SOINN ASC Error: same Speed: faster

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments: Artificial dataset 20 dataset Adjusted SOINN ASC Error: same Speed: faster

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments: Artificial dataset 21 dataset Adjusted SOINN ASC Error: better Speed: faster

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments: Artificial dataset 22 dataset Adjusted SOINN ASC Error: better Speed: faster

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments: Real dataset 23 Compression ratio (%) Speed up ratio (%)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments: Compare with other prototype-based classification method 24 Nearest Subclass Classifier (NSC) k-Means Classifier (KMC) k-NN Classifier (NNC) Learning Vector Quantization (LVQ)

Intelligent Database Systems Lab N.Y.U.S.T. I. M. Experiments: Compare with other prototype-based classification method 25

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 26

Intelligent Database Systems Lab N.Y.U.S.T. I. M. 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 27