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Neural Network

Neural Network. Developed by: Dr Eddie Ip Modified by: Dr Arif Ansari. Outline. Where in business is NN used? How does it work? A function approximation method Case study. NN: Overview. Wall Street’s “rocket science” Long history: 1950 Compete with expert system

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Neural Network

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  1. Neural Network Developed by: Dr Eddie Ip Modified by: Dr Arif Ansari

  2. Outline • Where in business is NN used? • How does it work? • A function approximation method • Case study

  3. NN: Overview • Wall Street’s “rocket science” • Long history: 1950 • Compete with expert system • Several generations of ANN • Lab research • Hype : thinking machine • Practical apps in fast chips

  4. NN: Overview • A handwritten recognition example • http://members.aol.com/Trane64/java/JRec.html

  5. NN: Overview • Learn by examples : mimic function of brain • Underlying technology: a network of “neurons” (nodes) connected by ‘nerves” (edges) • Each has a set of parameters • Each edge is weighted by the relative strength of that connection • Parameters & weights are given values after the network is “trained”

  6. Where can NN be used? • Supervised learning • classification (directed DM) • prediction (directed DM) • Unsupervised learning • clustering (undirected DM) • Self Organizing Map (SOM)

  7. Where can NN be used • real estate appraisal • NN learns by examples • Freddie Mac: Loan Prospector • Fraud detection • Master card • team up with Los Alamos Lab • use state-of-the-art technology that is deployed for military applications • report a saving of more than $50m using the fraud detection system

  8. Where can NN be used? • Direct mail • HNC’s Database Mining Marksman • for one bank, reduce cost by 50%, increase sales by 18% • system costs $48,000 to set up (1995) • financial service customer relationship management (reader) • e-customer relationship management • e.g. HNC, NeoVista, Agnos’s KnowledgeSTUDIO

  9. How does NN work? • A set of inputs (nodes) • A set of outputs (one to a few) • Model biological neurons

  10. How does NN work? • Inputs must be between 0 and 1 • Output is also between 0 and 1 • “Scaling” required for numerical values • (Value - min)/range

  11. How does NN work? • Topology / Architecture • input • output • hidden layer • connection

  12. Architecture of an artificial neural network Input 1 Input 2 Output Input 3 Input Layer Hidden Layer Output Layer

  13. How does NN work? • Action is in hidden layer • Each node combines inputs to give an output

  14. How does NN work? • How to combine? • combination function • transfer function

  15. How does NN work? • Combination function • Put several values into one • weighted sum • maximum

  16. How does NN work? • Transfer function • transfer value of combination function to output (of node) • sigmoid • turn the weighted sum back on 1 to 1 scale

  17. How does NN work? • Training = let it learn from examples • Weights adjusted by procedure calledbackward propagation

  18. How does NN work? • Training & testing sets • Overfitting problem

  19. How does NN work? • Overfit problem • Getting “too close” to data • NN pays attention to noise rather than signal • Result: poor performance when applied to new data set • Remedy: use a separate data set (testing set) to stop training

  20. How does NN work? • Evaluation set • Used for objectively evaluating performance of NN • Misclassification • Discrepancy between what it predicts and what is really observed • Performance metric based on learning sample too optimistic

  21. How does NN work? • Steps in applying NN (p.692) • Transform data • Select architecture • Train and test • Deploy

  22. How does NN work? • Devil is in the details • Training set should cover full range • Topology • Choosing appropriate input variables • e.g. use output from Decision Tree

  23. NN: function approximation • Relate the “inputs” and “outputs” • Mathematically a function approximation problem • NN combines “sigmoids” to produce nonlinear functions of any kind (almost)

  24. Glossary • NN: Neural Network • ANN: Artificial Neural Network • DM: Data Mining • SOM: Self Organizing Maps

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