Large Scale Multi-Label Classification

Large Scale Multi-Label Classification via MetaLabeler

Lei Tang Arizona State University

Suju Rajan and Vijay K. Narayanan Yahoo! Data Mining & Research

Yahoo! Data Mining & Research

Large Scale Multi-Label Classification

• Huge number of instances and categories• Common for online contents

Web Page Classification

Query Categorization

Video Annotation/Organization

Social Bookmark/Tag Recommendation


Challenges

• Multi-Class: thousands of categories• Multi-Label: each instance has >1 labels• Large Scale: huge number of instances and categories

– Our query categorization problem: 1.5M queries, 7K categories– Yahoo! Directory 792K docs, 246K categories in Liu et al. 05

• Most existing multi-label methods do not scale– structural SVM, mixture model, collective inference, maximum-

entropy model, etc.

• The simplest One-vs-Rest SVM is still widely used


One-vs-Rest SVMx1 C1, C3

x2 C1, C2, C4

x3 C2

x4 C2, C4

x1 +

x2 +

x3 -

x4 -

x1 -

x2 +

x3 +

x4 +

x1 +

x2 -

x3 -

x4 -

x1 -

x2 +

x3 -

x4 +

C1 C2 C3 C4

SVM1 SVM2 SVM3 SVM4

C1 C2C3 C4

Predict


One-vs-Rest SVM

• Pros:– Simple, Fast, Scalable– Each label trained independently, easy to parallel

• Cons:– Highly skewed class distribution (few +, many -)– Biased prediction scores

• Output reasonable good ranking (Rifkin and Klauta 04)– e.g. 4 categories C1, C2, C3, C4

– True Labels for x1: C1, C3

– Prediction Scores: {s1, s3} > {s2, s4}

• Predict the number of labels?


MetaLabeler Algorithm

1. Obtain a ranking of class membership for each instance

– Any genetic ranking algorithm can be applied– Use One-vs-Rest SVM

2. Build a Meta Model to predict the number of top classes

– Construct Meta Label– Construct Meta Feature– Build Meta Model


Meta Model – Training

Q2 = cotton children jeansLabels:

• Children clothing

Q3 = leather fashion in 1990sLabels:

• Fashion• Women Clothing• Leather Clothing

Q1 = affordable cocktail dressLabels:

• Formal wear• Women Clothing

Q1: 2Q2: 1Q3: 3

Meta dataQuery: #labels

Meta-ModelOne-vs-Rest

SVM

Clothing

WomenClothing

Formalwear Fashion

ChildrenClothing

Regression

Leather clothing

How to handle predictions like

2.5 labels?


Meta Feature Construction

• Content-Based– Use raw data– Raw data contains all the info

• Score-Based– Use prediction scores– Bias with scores might be learned

• Rank-Based– Use sorted prediction scores

C1 C2 C3 C40.9 -0.2 0.7 -0.6

C1 C2 C3 C4Meta Feature 0.9 -0.2 0.7 - 0.6

Meta Feature0.9 0.7 -0.2 -0.6


MetaLabeler Prediction

• Given one instance:– Obtain the rankings for all labels;– Use the meta model to predict the number of labels– Pick the top-ranking labels

• MetaLabeler– Easy to implement– Use existing SVM package/software directly– Can be combined with a hierarchical structure easily

• Simply build a Meta Model at each internal node


Baseline Methods

• Existing thresholding methods (Yang 2001)– Rank-based Cut (Rcut)

• output fixed number of top-ranking labels for each instance

– Proportion-based Cut• For each label, choose a portion of test instances as positive • Not applicable for online prediction

– Score-based Cut (Scut, aka. threshold tuning)• For each label, determine a threshold based on cross-validation• Tends to overfit and is not very stable

• MetaLabeler: A local RCut method– Customize the number of labels for each instance


Publicly Available Benchmark Data

• Yahoo! Web Page Classification– 11 data sets:

• each constructed from a top-level category• 2nd level topics are the categories

– 16-32k instances, 6-15k features, 14-23 categories– 1.2 -1.6 labels per instance, maximum 17 labels– Each label has at least 100 instances

• RCV1:– A large scale text corpus – 101 categories, 3.2 labels per instance– For evaluation purpose, use 3000 for training, 3000 for testing– Highly skewed distribution (some labels have only 3-4 instances)

http://www.kecl.ntt.co.jp/as/members/ueda/yahoo.tar.gz

http://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multilabel.html


MetaLabeler of Different Meta Features

• Which type of meta feature is more predictive?

• Content-based MetaLabeler outperforms other meta features

Exact Match Ratio Micro-F1 Macro-F130

35

40

45

50

55

60

65

Yahoo!

content score rank


30

40

50

60

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RCV1

content score rank


Performance Comparison

• MetaLabeler tends to outperform other methods


35

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65

Yahoo!

SVM RCutSCut MetaLabeler


30

40

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RCV1

SVM RCutSCut MetaLabeler


Bias with MetaLabeler

• The distribution of number of labels is imbalanced– Most instances have small number of labels;– Small portion of data instances have many more labels

• Imbalanced Distribution leads to bias in MetaLabeler– Prefer to predict lesser labels– Only predict many labels with strong confidence

1 2 3 4 5 6 70

2000

4000

6000

8000

10000

12000

Label Distribution on Yahoo! Society Data

Ground Truth MetaLabeler Prediction

Number of Labels

Freq

uenc

y


Scalability Study

• Threshold tuning requires cross-validation, otherwise overfit• MetaLabeler simply adds some meta labels and learn One-vs-

Rest SVMs

1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

200400600800

100012001400160018002000

Computation Time Comparison on Yahoo! Society Data

SVMMetaLabelerThreshold Tuning

Number of Samples for Training

Com

puta

tion

Tim

e (s

econ

ds)


Scalability Study (cond.)

• Threshold tuning: linearly increasing with number of categories in the data– E.g. 6000 categories -> 6000 thresholds to be tuned

• MetaLabeler: upper bounded by the maximum number of labels with one instance– E.g. 6000 categories – but one instance has at most 15 labels– Just need to learn additional 15 binary SVMs

• Meta Model is “independent” of number of categories


Application to Large Scale Query Categorization

• Query categorization problem:– 1.5 million unique queries: 1M for training, 0.5M for testing– 120k features– A 8-level taxonomy of 6433 categories

• Multiple labels – e.g. 0% interest credit card no transfer fee

• Financial Services/Credit, Loans and Debt/Credit/Credit Card/ Balance Transfer• Financial Services/Credit, Loans and Debt/Credit/Credit Card/ Low Interest Card• Financial Services/Credit, Loans and Debt/Credit/Credit Card/ Low-No-fee Card

1 label81%

2 labels16%

3+ labels3%

• 1.23 labels on average• At most 26 labels


Flat Model

• Flat Model: do not leverage the hierarchical structure– Threshold tuning on training data alone takes 40 hours to finish

while MetaLabeler costs 2 hours.

1 2 3 4 5 6 7 80

10

20

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100

SVMMetaLabelerThreshold Tuning

Depth

Mic

ro-F

1


Hierarchical Model - Training

Root

. . . . .. . . . . . . . . . . .

. . . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . .

Training Data

N

New Training Data

Step 1: Generate Training Data

Step 2: Roll up labels

Step 4: Train One vs. Rest SVMOther

Step 3: Create “Other” Category


Hierarchical Model - Prediction

Root

. . . . . . . . . . . . . .

. . . . . . . . . . . . . .

. . . . . . . . . . . . . . . . . . . . . . . . . .

Query q Predict using SVMs trained at root level

Query q

Query q

Stop !!!

• Stop if reaching a leaf node or “other” category

m1

m2

m3

m2 m3m4

c1

c2

c3 Other

Stop !!!


Hierarchical Model + MetaLabeler

• Precision decrease by 1-2%, but recall is improved by 10% at deeper levels.

1 2 3 4 5 6 7 860

65

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95

100

MetaLabeler-PrecisionMetaLabeler-RecallSVM-PrecisionSVM-Recall

Depth

Perfo

rman

ce


Features in MetaLabelerFeature Related Categories

Overstock.com –Mass Merchants/…/discount department stores–Apparel & Jewelry –Electronics & Appliances –Home & Garden–Books-Movies-Music-Tickets

Blizard –Toys & Hobbies/…/Video Game–Computing/…/Computer Game Software–Entertainment & Social Event/…/Fast Food Restaurant–Reference/News/Weather Information

Threading – Books-Movies-Music-Tickets/…/Computing Books– Computing/…/Programming– Health and Beauty/…/Unwanted Hair– Toys and Hobbies/…/Sewing


Conclusions & Future Work

• MetaLabeler is promising for large-scale multi-label classification– Core idea: learn a meta model to predict the number of labels– Simple, efficient and scalable– Use existing SVM software directly– Easy for practical deployment

• Future work– How to optimize MetaLabeler for desired performance ?

• E.g. > 95% precision

– Application to social networking related tasks

Questions?


References

• Liu, T., Yang, Y., Wan, H., Zeng, H., Chen, Z., and Ma, W. 2005. Support vector machines classification with a very large-scale taxonomy. SIGKDD Explor. Newsl. 7, 1 (Jun. 2005), 36-43.

• Rifkin, R. and Klautau, A. 2004. In Defense of One-Vs-All Classification. J. Mach. Learn. Res. 5 (Dec. 2004), 101-141.

• Yang, Y. 2001. A study of thresholding strategies for text categorization. In Proceedings of the 24th Annual international ACM SIGIR Conference on Research and Development in information Retrieval (New Orleans, Louisiana, United States). SIGIR '01. ACM, New York, NY, 137-145.

Large Scale Multi-Label Classification

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