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Event classification & prediction using support vector machine

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Classification and prediction of two different real time application using support vector machine.

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Event classification & prediction using support vector machine

  1. 1. By Ruta Ashok Kambli (122071013) Event Classification & Prediction Using Support Vector Machine
  2. 2. Scope of Presentation  Introduction  Support Vector Machine(SVM)  Hard-margin SVM  Soft -margin SVM  Kernels  Multiclass classification  SVM Model Selection  Case Studies & Results  Conclusion
  3. 3. Introduction  Classification & Prediction  Machine Learning  Support Vector Machine
  4. 4. Machine learning Unsupervised learning Clustering K-mean Herarchial Neural network Supervised learning Classification SVM Neural Network Decision tree Regression
  5. 5. Support Vector Machines • Supervised machine learning model. • Analyse data and recognize patterns. • Used for classification and regression analysis.
  6. 6. Binary Classification Consider training data set (𝑥𝑖, 𝑦𝑖) for (i = 1, . . . , M), with 𝑥𝑖 ∈ ℝ 𝑑 and 𝑦𝑖 ∈ {−1, 1}, learn a classifier D(x) such that, 𝐷(𝑥𝑖) ≥ 1, 𝑓𝑜𝑟 𝑦𝑖 = 1 ≤ −1, 𝑓𝑜𝑟 𝑦𝑖 = −1 ……(1) ie. 𝑦𝑖 𝐷 𝑥𝑖 ≥ 1 for a correct classification.
  7. 7. Binary Classification x1 x2 denotes +1 denotes -1
  8. 8.  How would you classify these points using a linear discriminant function in order to minimize the error rate? Binary Classificationdenotes +1 denotes -1 x1 x2  Infinite number of answers!
  9. 9.  How would you classify these points using a linear discriminant function in order to minimize the error rate? Binary Classificationdenotes +1 denotes -1 x1 x2  Infinite number of answers!
  10. 10.  How would you classify these points using a linear discriminant function in order to minimize the error rate? Binary Classificationdenotes +1 denotes -1 x1 x2  Infinite number of answers!
  11. 11. x1 x2 How would you classify these points using a linear discriminant function in order to minimize the error rate? Binary Classificationdenotes +1 denotes -1  Infinite number of answers!  Which one is the best?
  12. 12. Binary Classification “safe zone”  We have to find out the optimal hyperplane with the maximum margin.  Margin is defined as the width that the boundary could be increased by before hitting a data point  Why it is the best?  Robust to outliners and thus strong generalization ability. Margin x1 x2 denotes +1 denotes -1
  13. 13. Hard-margin SVM
  14. 14. Minimise : 𝑄 𝑤, 𝑏 = 1 2 𝑤 2 …….(2) Subject to: 𝑦𝑖 𝑤 𝑇 𝑥𝑖 + 𝑏 ≥ 1 𝑓𝑜𝑟 𝑖 = (1, … … , 𝑀) …….(3) Q(w, b,𝛼)=𝑊 𝑇 𝑊 − 𝑖=1 𝑀 𝛼𝑖 𝑦𝑖 𝑤 𝑇 𝑥𝑖 + 𝑏 − 1 ……(4) Where 𝛼 = (𝛼𝑖, … … 𝛼 𝑀) and 𝛼𝑖 are the nonnegative Lagrange multipliers. • The optimal solution of (4) is given by the saddle point. • Where (4) is minimized with respect to w • Maximized with respect to 𝛼𝑖 (≥ 0) • Maximized or minimized with respect to b according to the sign 𝑖=1 𝑀 𝛼𝑖 𝑦𝑖
  15. 15. Soft- margin SVM 𝑦𝑖 𝑤 𝑇 𝑥𝑖 + 𝑏 ≥ 1 − 𝜉𝑖 𝑓𝑜𝑟 𝑖 = 1, … … , 𝑀 …….(7)
  16. 16. Soft margin SVM 𝑚𝑖𝑛𝑖𝑚𝑖𝑠𝑒 𝑄 𝑤, 𝑏, 𝜉 = 1 2 𝑤 2 + 𝐶 𝑃 𝑖=1 𝑀 𝜉𝑖 𝑃 ……..(5) 𝑆𝑢𝑏𝑗𝑒𝑐𝑡 𝑡𝑜 𝑦𝑖 𝑤 𝑇 𝑥𝑖 + 𝑏 ≥ 1 − 𝜉𝑖 𝑓𝑜𝑟 𝑖 = 1, … … , 𝑀 ….(6) 𝑄 𝑤, 𝑏, 𝛼, 𝛽 = 1 2 𝑤 2 + 𝐶 𝑖=1 𝑀 𝜉𝑖 − 𝑖=1 𝑀 𝛼𝑖 𝑦𝑖 𝑤 𝑇 𝑥𝑖 + 𝑏 − 1 + 𝜉𝑖 − 𝑖=1 𝑀 𝛽𝑖 𝜉𝑖 ……(7)
  17. 17. Kernels Types of Kernel Function Polynomial Radial Base function(RBF) Sigmoid
  18. 18. Multiclass Classification  Initially SVM is Binary Classifier.  Most of the practical applications involve multiclass classification.  One against One Approach.  If n is the number of classes, we generate n(n-1)/2 models.  It is not practical for large-scale linear classification.
  19. 19. SVM Model Margin Parameter (C) Selection
  20. 20. SVM Model Kernel Parameter Selection
  21. 21. K-fold Cross Validation  Create a K-fold partition of the dataset.  For each of K experiments, use K-1 folds for training and the remaining one for testing.  The advantage of K-Fold Cross validation is that all the examples in the dataset are eventually used for both training and testing
  22. 22. Classification using SVM Data acquisition using NI-Elvis Feature selection using Wavelate Feature classification using SVM
  23. 23. Data acquisition using NI-Elvis  Two connectors are connected to Flexor Digitorum supercialis (FDS) muscle.  The readings are taken for different hand movements.
  24. 24. Data acquisition using NI-Elvis This is time verses amplitude graph of hand movement data.  Class 1 :open hand  Class 2 : closed hand  Class 3 :wrist flexion
  25. 25. Results (training & testing) Subject Training Accuracy (%) Testing Accuracy(%) Male1 89.5833 86.3636 Male2 93.75 79.1667 Female 1 90 80
  26. 26. Blackout Prediction Using SVM
  27. 27. Probabilistic Model
  28. 28. Kernel Selection Kernel Training Accuracy % Testing Accuracy% Polynomial 100 94.44 Radial 100 100 Sigmoid 52.63 38.89
  29. 29. Margin Parameter Selection
  30. 30. Kernel Parameter Selection
  31. 31. Conclusion  Results of first case study show that, single channel surface Electromyogram analysis is simple, less expensive and effective.  The second case study shows, using blackout prediction model we can predict blackout before it occurs.  Here output of SVM is given to emergency control system, which initiates the prevention mechanism against the blackout.
  32. 32. Refferences 1. “Support Vector Machines for Pattern Classification” by Shigeo Abe 2. “Classification of low-level finger contraction from single channel Surface EMG” by Vijay Pal Singh and Dinesh Kant Kumar 3. “Fault Location in Power Distribution System with Distributed Generation Using Support Vector Machine,” by Agrawal, R.Thukaram 4. M. R. Ahsan, M. I. Ibrahimy, and O. O. Khalifa, “EMG signal classication for human computer interaction: A review,"European Journal of Scientic Research, vol. 33, no. 3, pp. 480-501, 2009.
  33. 33. References 5. J. Kim, S. Mastnik, and E. Andr,”EMG-based hand gesture recognition for realtime biosignal interfacing,"13th international conference on Intelligent user interfaces, 2008, pp.3039. 6. K. Englehart and B. Hudgins, “A robust, real- time control scheme for multifunction myoelectric control,"Biomedical Engineering, IEEE Transactions on, vol. 50, no. 7, pp. 848854, 2003. 7. C Rudin, D Waltz, and R N Anderson, “Machine learning for the new york city power grid,"IEEE Trans. on Pattern analysis and machine intelligence , VOL. 34, NO. 2, February 2011
  34. 34. THANK YOU

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