Event classification & prediction using support vector machine
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This document provides an overview of event classification and prediction using support vector machines (SVM). It begins with an introduction to classification, machine learning, and SVM. It then discusses binary classification with SVM, including hard-margin and soft-margin SVM, kernels, and multiclass classification. The document presents case studies on classifying hand movements from electromyography data and predicting power grid blackouts using SVM. It concludes that SVM is effective for these classification tasks and can initiate prevention mechanisms for predicted events.
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Event classification & prediction using support vector machine
- 1. By Ruta Ashok Kambli (122071013) Event Classification & Prediction Using Support Vector Machine
- 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. Introduction Classification & Prediction Machine Learning Support Vector Machine
- 5. Support Vector Machines • Supervised machine learning model. • Analyse data and recognize patterns. • Used for classification and regression analysis.
- 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.
- 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. 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. 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. 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. 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. Hard-margin SVM
- 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. Soft- margin SVM 𝑦𝑖 𝑤 𝑇 𝑥𝑖 + 𝑏 ≥ 1 − 𝜉𝑖 𝑓𝑜𝑟 𝑖 = 1, … … , 𝑀 …….(7)
- 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. Kernels Types of Kernel Function Polynomial Radial Base function(RBF) Sigmoid
- 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. SVM Model Margin Parameter (C) Selection
- 20. SVM Model Kernel Parameter Selection
- 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. Classification using SVM Data acquisition using NI-Elvis Feature selection using Wavelate Feature classification using SVM
- 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. 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
- 26. Results (training & testing) Subject Training Accuracy (%) Testing Accuracy(%) Male1 89.5833 86.3636 Male2 93.75 79.1667 Female 1 90 80
- 30. Kernel Selection Kernel Training Accuracy % Testing Accuracy% Polynomial 100 94.44 Radial 100 100 Sigmoid 52.63 38.89
- 33. 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.
- 34. 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.
- 35. 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
- 36. THANK YOU
Editor's Notes
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