1. Using Error-Correcting Codes For Text Classification Rayid Ghani rayid@cs.cmu.edu Center for Automated Learning & Discovery, Carnegie Mellon University This presentation can be accessed at http://www.cs.cmu.edu/~rayid/icmltalk

2. Outline Review of ECOC Previous Work Types of Codes Experimental Results Semi-Theoretical Model Drawbacks Conclusions & Work in Progress

3. Overview of ECOC Decompose a multiclass problem into multiple binary problems The conversion can be independent or dependent of the data (it does depend on the number of classes) Any learner that can learn binary functions can then be used to learn the original multivalued function

4. ECOC-Picture AB C

5. Training ECOC Given m distinct classes Create an m x n binary matrix M. Each class is assigned ONE row of M. Each column of the matrix divides the classes into TWO groups. Train the Base classifier to learn the n binary problems.

6. Testing ECOC To test a new instance Apply each of the n classifiers to the new instance Combine the predictions to obtain a binary string(codeword) for the new point Classify to the class with the nearest codeword (usually hamming distance is used as the distance measure)

7. Previous Work Combine with Boosting – ADABOOST.OC (Schapire, 1997)

8. Types of Codes Random Algebraic Constructed/Meaningful

9. Experimental Setup Generate the code Choose a Base Learner

10. Dataset Industry Sector Dataset Consists of company web pages classified into 105 economic sectors Standard stoplist No Stemming Skip all MIME and HTML headers Experimental approach similar to McCallum et al. (1997) for comparison purposes.

11. Results Classification Accuracies on five random 50-50 train-test splits of the Industry Sector dataset with a vocabulary size of 10000. ECOC - 88% accurate!

12. How does the length of the code matter? Table 2: Average Classification Accuracy on 5 random 50-50 train-test splits of the Industry Sector dataset with a vocabulary size of 10000 words selected using Information Gain. Longer codes mean larger codeword separation The minimum hamming distance of a code C is the smallest distance between any pair of distance codewords in C If minimum hamming distance is h, then the code can correct  (h-1)/2 errors

13. Theoretical Evidence Model ECOC by a Binomial Distribution B(n,p)n = length of the code p = probability of each bit being classified incorrectly

15. Size Matters?

16. Size does NOT matter!

17. Choosing Codes

18. Interesting Observations NBC does not give good probabilitiy estimates- using ECOC results in better estimates.

19. Drawbacks Can be computationally expensive Random Codes throw away the real- world nature of the data by picking random partitions to create artificial binary problems

20. Conclusion Improves Classification Accuracy considerably! Extends a binary learner to a multiclass learner Can be used when training data is sparse

21. Future Work Use meaningful codes (hierarchy or distinguishing between particularly difficult classes) Use artificial datasets Combine ECOC with Co-Training or Shrinkage Methods Sufficient and Necessary conditions for optimal behavior