Introduction to Support Vector Machines for Data Mining

1. Introduction to Support Vector Machines for Data Mining Mahdi Nasereddin Ph.D. Pennsylvania State University School of Information Sciences and Technology

2. Agenda • Introduction • Support Vector Machines • Preliminary Experimentation • Conclusion • Questions?

3. Data Mining Techniques: • Neural Networks • Decision Trees • Multivariate Adaptive Regression Splines (MARS) • Rule Induction • Nearest Neighbor Method and discriminant analysis • Genetic Algorithms • Support Vector Machines

4. Support Vector Machines • First introduced by Vapnik and Chervonenkis in COLT-92 • Bases on Statistical Learning Theory • Applications • Basic Theory • Classification • Regression

5. Successful Applications of SVMS • Protein Structure Prediction http://www.cs.umn.edu/~hpark/papers/surface.pdf • Intrusion Detection www.cs.nmt.edu/~IT • Handwriting Recognition • Detecting Steganography in digital images http://www.cs.dartmouth.edu/~farid/publications/ih02.html

6. Successful Applications of SVMS • Breast Cancer Prognosis: Chemotherapy Effect on Survival Rate (Lee, Mangasarian and Wolberg, 2001) • Particle and Quark-Flavour Identification in High Energy Physics (http://wwwrunge.physik.uni-freiburg.de/preprints/EHEP9901.ps) • Function Approximation

7. Support Vector Machines(Linearly separable case)

8. Support Vector Machines(Linearly separable case)

9. Support Vector Machines(Linearly separable case)

10. Non-Linearly separable case

11. SVM for Regression • In case of regression, the goal is to construct a hyperplane that is close to as many points as possible. • For both classification and regression, learning is done via quadratic programming (one optimum point)

12. Strengths and Weaknesses of SVM • Strengths • Training is relatively easy • No local optimal, unlike in neural networks • It scales relatively well to high dimensional data • Weaknesses • Need a “good” kernel function

13. Preliminary Experimentation: Forecasting GDP using Oil Prices (with F. Malik) • Forecasting model • Objective: To predict the Gross Domestic Product (GDP) for the next quarter using • Oil prices (including time lag) • GDP time

14. Data Set • We looked at quarterly Oil prices and GDP data • January 1947 – December 2002 • Oil price data were obtained from Bureau of Labor Statistics • GDP data were obtained from the Bureau of Economic Analysis. • We used the growth rate of GDP and the growth rate of oil prices.

15. Models • Neural Networks • Back-propagation • One hidden layer • Delta rule was used for training • LS-SVM (Van Gestel, 2001) • Matlab toolbox

16. Experimentation • Created the training data to predict the last 40 quarters GDP (test data) • Trained the neural network and the SVM • Used the model to predict GDP, and calculated the error of prediction

17. Results

18. Good References • Introductions • Martin Law, “An Introduction to Support Vector Machines” • Andrew More, “Support Vector Machines” www.cs.cmu.edu/~awm • N. Cristianini www.support-vector.net/tutorial.html • In depth • Support Vector Machines book www.support-vector.net

19. Questions • E-mail: mxn16@psu.edu • Presentation will be posted (by Friday) at http://www.bklv.psu.edu/faculty/nasereddin