Using Error-Correcting Codes For Text Classification Rayid Ghani This presentation can be accessed at

Outline Introduction to ECOC Intuition & Motivation Some Questions? Experimental Results Semi-Theoretical Model Types of Codes Drawbacks Conclusions

Introduction Decompose a multiclass classification problem into multiple binary problems One-Per-Class Approach (moderately expensive) All-Pairs (very expensive) Distributed Output Code (efficient but what about performance?) Error-Correcting Output Codes (?)

Is it a good idea? Larger margin for error since errors can now be “corrected” One-per-class is a code with minimum hamming distance (HD) = 2 Distributed codes have low HD The individual binary problems can be harder than before Useless unless number of classes > 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 classifiers to learn the n binary problems.

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)

ECOC - Picture ABCDABCD A D C B f 1 f 2 f 3 f 4 f 5

ECOC - Picture ABCDABCD A D C B f 1 f 2 f 3 f 4 f 5

ECOC - Picture ABCDABCD A D C B f 1 f 2 f 3 f 4 f 5

ECOC - Picture ABCDABCD A D C B f 1 f 2 f 3 f 4 f 5 X

Questions? How well does it work? How long should the code be? Do we need a lot of training data? What kind of codes can we use? Are there intelligent ways of creating the code?

Previous Work Combine with Boosting – ADABOOST.OC (Schapire, 1997), (Guruswami & Sahai, 1999) Local Learners (Ricci & Aha, 1997) Text Classification (Berger, 1999)

Experimental Setup Generate the code BCH Codes Choose a Base Learner Naive Bayes Classifier as used in text classification tasks (McCallum & Nigam 1998) Naive Bayes Classifier

Dataset Industry Sector Dataset Consists of company web pages classified into 105 economic sectors Standard stoplist No Stemming Skip all MIME headers and HTML tags Experimental approach similar to McCallum et al. (1998) for comparison purposes.

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

Results Industry Sector Data Set Naïve Bayes Shrinkage 1 ME 2 ME/ w Prior 3 ECOC 63-bit 66.1%76%79%81.1%88.5% ECOC reduces the error of the Naïve Bayes Classifier by 66% 1.(McCallum et al. 1998) 2,3. (Nigam et al. 1999)

The Longer the Better! Table 2: Average Classification Accuracy on 5 random train-test splits of the Industry Sector dataset with a vocabulary size of 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

Size Matters?

Size does NOT matter!

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

Semi-Theoretical Model Model ECOC by a Binomial Distribution B(n,p) n = length of the code p = probability of each bit being classified incorrectly # of BitsH min E max P ave Accuracy

Semi-Theoretical Model Model ECOC by a Binomial Distribution B(n,p) n = length of the code p = probability of each bit being classified incorrectly # of BitsH min E max P ave Accuracy

Types of Codes Data-Independent Data-Dependent Algebraic Random Hand-Constructed Adaptive

What is a Good Code? Row Separation Column Separation (Independence of errors for each binary classifier) Efficiency (for long codes)

Choosing Codes RandomAlgebraic Row SepOn Average For long codes Guaranteed Col SepOn Average For long codes Can be Guaranteed EfficiencyNoYes

Experimental Results CodeMin Row HD Max Row HD Min Col HD Max Col HD Error Rate 15-Bit BCH % 19-Bit Hybrid % 15-bit Random 2 (1.5) %

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

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

Future Work Combine ECOC with Co-Training Automatically construct optimal / adaptive codes

Conclusion Improves Classification Accuracy considerably! Can be used when training data is sparse Algebraic codes perform better than random codes for a given code lenth Hand-constructed codes are not the answer