1 / 18

A Practical Guide to SVM

A Practical Guide to SVM. Yihua Liao Dept. of Computer Science 2/3/03. Outline. Support vector machine basics GIST LIBSVM (SVMLight). Classification problems. Given: n training pairs, (<x i >, y i ), where

lydie
Télécharger la présentation

A Practical Guide to SVM

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. A Practical Guide to SVM Yihua Liao Dept. of Computer Science 2/3/03

  2. Outline • Support vector machine basics • GIST • LIBSVM (SVMLight)

  3. Classification problems • Given: n training pairs, (<xi>, yi), where <xi>=(xi1, xi2,…,xil) is an input vector, and yi=+1/-1, corresponding classification H+ /H- • Out: A label y for a new vector x

  4. Support vector machines Goal: to find discriminator That maximize the margins

  5. A little math • Primal problem • Decision function

  6. Example • Functional classifications of Yeast genes based on DNA microarray expression data. • Training dataset • genes that are known to have the same Function f • genes that are known to have a different function than f

  7. Gist • http://microarray.cpmc.columbia.edu/gist/ • Developed by William Stafford Noble etc. • Contains tools for SVM classification, feature selection and kernel principal components analysis. • Linux/Solaris. Installation is straightforward.

  8. Data files • Sample.mtx(tab-delimited, same for testing) gene alpha_0X alpha_7X alpha_14X alpha_21X … YMR300C -0.1 0.82 0.25 -0.51 … YAL003W 0.01 -0.56 0.25 -0.17 … YAL010C -0.2 -0.01 -0.01 -0.36 … … • Sample.labels gene Respiration_chain_complexes.mipsfc YMR300C -1 YAL003W 1 YAL010C -1

  9. Usage of Gist • $compute-weights -train sample.mtx -class sample.labels > sample.weights • $classify -train sample.mtx -learned sample.weights -test test.mtx > test.predict • $score-svm-results -test test.labelstest.predict sample.weights

  10. Test.predict # Generated by classify # Gist, version 2.0 …. gene classification discriminant YKL197C -1 -3.349 YGL022W -1 -4.682 YLR069C -1 -2.799 YJR121W 1 0.7072

  11. Output of score-svm-results Number of training examples: 1644 (24 positive, 1620 negative) Number of support vectors: 60 (14 positive, 46 negative) 3.65% Training results: FP=0 FN=3 TP=21 TN=1620 Training ROC: 0.99874 Test results: FP=12 FN=1 TP=9 TN=801 Test ROC: 0.99397

  12. Parameters • compute-weights • -power <value> • -radial -widthfactor <value> • -posconstraint <value> • -negconstraint <value> …

  13. Rules of thumb • Radial basis kernel usually performs better. • Scale your data. scale each attribute to [0,1] or [-1,+1] to avoid over-fitting. • Try different penalty parameters C for two classes in case of unbalanced data.

  14. LIBSVM • http://www.csie.ntu.edu.tw/~cjlin/libsvm/ • Developed by Chih-Jen Lin etc. • Tools for (multi-class) SV classification and regression. • C++/Java/Python/Matlab/Perl • Linux/UNIX/Windows • SMO implementation, fast!!!

  15. Data files for LIBSVM • Training.dat +1 1:0.708333 2:1 3:1 4:-0.320755 -1 1:0.583333 2:-1 4:-0.603774 5:1 +1 1:0.166667 2:1 3:-0.333333 4:-0.433962 -1 1:0.458333 2:1 3:1 4:-0.358491 5:0.374429 … • Testing.dat

  16. Usage of LIBSVM • $svm-train -c 10 -w1 1 -w-1 5 Train.dat My.model - train classifier with penalty 10 for class 1 and penalty 50 for class –1, RBK • $svm-predict Test.dat My.model My.out • $svm-scaleTrain_Test.dat > Scaled.dat

  17. Output of LIBSVM • Svm-train optimization finished, #iter = 219 nu = 0.431030 obj = -100.877286, rho = 0.424632 nSV = 132, nBSV = 107 Total nSV = 132

  18. Output of LIBSVM • Svm-predict Accuracy = 86.6667% (234/270) (classification) Mean squared error = 0.533333 (regression) Squared correlation coefficient = 0.532639 (regression) • Calculate FP, FN, TP, TN from My.out

More Related