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Understanding SANN: Enhanced Classification with Artificial Neural Networks

This document explores the concept of SANN (Subsequent Artificial Neural Networks) and outlines its advantages over traditional ANN approaches. Key topics include terminology, clustering methods, dimensionality reduction, and various training techniques such as backpropagation. The effectiveness of SANN in classifying complex datasets, including detailed comparisons with All Pairs methods, is evaluated. We discuss tools for implementation, highlight results demonstrating improved accuracy and speed, and provide insights into the impacts of hyperplanes and inputs on the neural network's learning processes.

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Understanding SANN: Enhanced Classification with Artificial Neural Networks

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  1. … SANN … Classification … Classification with Subsequent Artificial Neural Networks Linder, Dew, Sudhoff, Theegarten, Remberger, PÖppl, Wagner Brian Selinsky

  2. Outline • Terminology • SANN vs Other ANN approaches • SANN vs All Pairs • Results

  3. Clustering Dimensionality NN vs. ANN Neuron Synapse Thresholds Weights Training Learning Rate Backpropagation Input Standardization Normalization Hidden Layers Approaches MLP One vs All All Pairs SANN Tools Terminology

  4. Clustering O X O X O O X X O X O O X X O X O X O X

  5. Clustering O X O Y X O Y O X X O Y X Y Y O O X X O Y Y X O X Y Y O X Y

  6. Clustering O X O Y X O Y O X X O Y X Y Y O O X X O Y Y X O X Y Y O X Y

  7. Dimensionality • Inputs of interest • Hyperplanes • Dr Frisinas’ data • 4 Clusters • 22690 Inputs of interest • 22690 Dimensional Data • 3 or 4 - 22689 Dimensional Hyperplanes

  8. Dimensionality • Neural Nets convert input dimensionality to 1 number! 1 0 -1

  9. ANN vs. NN • Semantics • Artificial Neural Net • Meant to simulate how the brain functions • Brain is a network of neurons • Brain is the natural neural net • I use NN

  10. Neural Net Class 1 Neural Net Black Box (Some magic happens here) Class 2 Class 3

  11. Neuron Neural Net Class 1 Calculate Summation Compare to Threshold Class 2 Class 3

  12. C1 C T C2 C3 Neural Network N Neural Net N N N N N N

  13. C1 C T C2 C3 Neural Network Inputs & Processing  N Neural Net N N N N N N  Learning

  14. C1 C T C2 C3 Training Inputs & Processing  N Neural Net Training Set N N N N N N  Learning

  15. What gets trained • Threshold • Categorization • Weight • Impact of an input to a neuron • Proportionality • Learning Rate • Effect on weights • Effect on speed of training

  16. C1 C T C2 C3 How? - Backpropagation Inputs & Processing  N Neural Net Training Set N N N N N N  Learning

  17. Input Data • Data 1 Range 12000 – 500000 • Data 2 Range 1.0 – 1.5 • Standardizing or normalizing data makes weights more consistent and more accurate

  18. Approaches • Multi-layer Perceptron (MLP) • Subdividing the problem • One vs. All • All Pairs • SANN

  19. One vs. All Class A ANN Not Class A ANN Class B Not Class B Class C ANN Not Class C

  20. All Pairs Class A ANN Class A Class B Class C Class A ANN Class C Class B Class B ANN Class C

  21. SANN ANN A vs B Class A .12 ANN Class B .88 Class C .91 ANN A vs C Class B .90 ANN B vs C Final Values Class A .12 Class B .90 Class C .89 Class C .89

  22. Results • Increased data & nodes • Increased noise • Subdividing NNs increases accuracy • All Pairs vs SANN • All Pairs more accurate • SANN faster

  23. Tools (FYI) • MatLab (Neural Network Toolbox) • On CS System (Unix and Windows) • NeuroSolutions • 60 day free trial (Windows) • Joone • Free (Platform Independent)

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