Hierarchical Classification by Jurgen Van Gael
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Hierarchical Classification by Jurgen Van Gael
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Hierarchical Classification by Jurgen Van Gael
- 1. Hierarchical Classification Jurgen Van Gael - .
- 2. About • Computer Scientist w/ background in ML. • London Machine Learning Meetup. • Founder of Math.NET numerical library. • Previously @ Microsoft Research. • Data science team lead at Rangespan.
- 4. Taxonomy Classification • Input: raw product data • Output: classification models, classified product data ROOT Electronics Audio Audio Cables Amps … Computers … Clothing Pants T-‐‑Shirts … Toys Model Rockets … …
- 7. Name: INK-M50 Black Ink Cartridge (600 pages) Manufacturer: Samsung Description: null Label: toner-inkjet-cartridges "category": "toner-inkjet-cartridges”, "features": ["cartridge", "samsung", "black", "ink", "ink-m50", "pages”] Feature Extraction: • Text cleaning (stopword, lexicalisation) • Unigram + Bigram Features • LDA Topic Features Data Collection Feature Extraction Training Testing Labelling
- 9. Training, Testing & Labelling
- 10. Hierarchical Classification D A C B E D A C E B 4 (5) way multiclass classification
- 11. Hierarchical Classification D A C B E D A C B E 2 + 3 way multiclass classification
- 12. Naïve Bayes Neural Network Logistic Regression Support Vector Machines … ?
- 13. Logistic Regression -‐‑ Model word printer-‐‑ ink printer-‐‑hardware cartridge 4.0 0.3 the 0.0 0.0 samsung 0.5 0.5 black 0.5 0.3 printer -‐‑1.0 2.0 ink 5.0 -‐‑1.7 … … … For each class For each feature Add the weight Exponentiate & Normalize 10.0 Σ= -‐‑0.6 Pr= 0.99997 0.0003 Data Collection Feature Extraction Training Testing Labelling
- 14. Logistic Regression -‐‑ Inference • Optimise using Wapiti. • Hyperparameter optimisation using grid search. • Using development set to stop training? Data Collection Feature Extraction Training Testing Labelling
- 16. ROOT Electronics Clothing Data Collection Feature Extraction Training Testing Labelling
- 17. Cross Validation Calibration • Estimate classifier errors. • DO NOT o Test on training data. o Leave data aside. • Are my probability estimates correct. • Computation: o Take x data points with p(.|x) = 0.9, o Check that about 90% of labels were correct. Data Collection Feature Extraction Training Testing Labelling Training Data Error = 1.2% Error = 1.1% Error = 1.2% Error = 1.2% Error = 1.3% = Error = 1.2%
- 18. Data Collection Feature Extraction Training Testing Labelling ROOT Electronics Clothing Using Bayes rule to chain classifiers:
- 19. Active Learning
- 21. ROOT Electronics Clothing p(electronics|{text}) = 0.1 Data Collection Feature Extraction Training Testing Labelling
- 22. • High probability datapoints o Upload to production • Low probability datapoints o Subsample o Acquire more labels Data Collection Feature Extraction Training Testing Labelling ROOT Electronics Clothing p(electronics|{text}) = 0.1 e.g. Mechanical Turk
- 23. Implementation
- 24. Implementation MongoDB S3 Raw S3 Training Data S3 Models 1. JSON export 2. Feature Extraction 3. Training 4. Classification
- 25. Training MapReduce • Dumbo on Hadoop • 2000 classifiers • 5 fold CV (+ full) • 20 hypers on grid = 200.000 training runs
- 26. Labelling • 128 chunks • Full Cascade each chunk D A CB E Chunk 1 Chunk 2 Chunk 3 Chunk N … D A CB ED A CB ED A CB E
- 27. Thoughts • Extra’s: o Partial labeling: stop when probability becomes low. o Data ensemble learning. • Most time spent feature engineering. • Tie the parameters of the classifiers? o Frustratingly easy domain adaptation, Hal Daume III • Partially flattening the hierarchy for training?