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Paper and experiments in Multi-Class Learning: From Theory to Algorithm, which have been accepted by NIPS 2018
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paper
utils
.gitignore
OneVsRest.ipynb
README.md
Test_Conv_MKL.m
Test_GMNP.py
Test_Gaussian_Kernel.m
Test_LMC.py
Test_MC_MKL_1.py
Test_MC_MKL_2.py
Test_OneVsOne.m
Test_OneVsRest.m
Test_SMSD_MKL.m
Test_UFO_MKL.m
class_tools.py
class_tools.pyc
feedback.txt
shogun_tools.ipynb
shogun_tools.pyc

README.md

Multi-Class Learning: From Theory to Algorithm

Links

[PDF] [Code] [Poster] [Slide] [3-minute video]

Supplementary file

Appendixes mentioned in the paper are attached in MC-supplementary.pdf

Usage of source code

Code used in experiments locates in ./code

Enviroment

We do experiments based on following softwares:

  1. Python 2.7
  2. MATLAB R2017b
  3. DOGMA toolbox from http://dogma.sourceforge.net/
  4. SHOGUN-6.1.3 from https://github.com/shogun-toolbox/shogun
  5. LIBSVM Tools from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/
  6. sklearn for python

Data sets

  1. plant, psortPos, psortNeg and nonpl from http://www.raetschlab.org/suppl/protsubloc
  2. others from https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/

Steps

  1. Download data sets and move dataName.phylpro.mat, label_dataName.mat and dataName.scale to code/data/
  2. Create Gaussian kernels: change variable file_list in Test_Gaussian_Kernel.m and run
  3. Run following methods
  • SMSD-MKL: change variables of data_sets in Test_SMSD_MKL.m and run
  • Conv-MKL: change variables of data_sets in Test_Conv_MKL.m and run
  • LMC: change variables of data_sets in Test_LMC.py and run
  • OneVsOne: change variables of data_sets in Test_OneVsOne.m and run
  • OneVsRest: change variables of data_sets in Test_OneVsOne.m and run
  • GMNP: change variables of data_sets in Test_GMNP.py and run
  • l1 MC-MKL: change variables of data_sets in Test_MC_MKL_1.py and run
  • l2 MC-MKL: change variables of data_sets in Test_MC_MKL_2.py and run
  • UFO-MKL: change variables of data_sets in Test_UFO_MKL.m and run
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