Special topics on text mining [ Part I: text classification ]

1. Special topics on text mining[Part I: text classification] Hugo Jair Escalante, Aurelio Lopez, Manuel Montes and Luis Villaseñor

2. Multi label text classification Hugo Jair Escalante, Aurelio Lopez, Manuel Montes and Luis Villaseñor Most of this material was taken from: G. Tsoumakas, I. Katakis and I. Vlahavas. Mining multi-label data.Data Mining and Knowledge Discovery Handbook, Part 6, O. Maimon, L. Rokach (Ed.), Springer, 2nd edition, pp. 667-685, 2010.

3. Machine learning approach to TC • Develop automated methods able to classify documents with a certain degree of success Labeled document Trained machine ? Training documents (Labeled) Learning machine (an algorithm) Unseen (test, query) document

4. What is a learning algorithm? • A function: • Given:

5. Binary vs multiclass classification • Binary classification: each document can belong to one of two classes. • Multiclass classification: each document can belong to one of K classes.

6. Classification algorithms • (Some) classification algorithms for TC : • Naïve Bayes • K-Nearest Neighbors • Centroid-based classification • Decision trees • Support Vector Machines • Linear classifiers (including SVMs) • Boosting, bagging and ensembles in general • Random forest • Neural networks Some of this methods were designed for binary classification problems

7. Linear models • Classification of DNA micro-arrays ? x2 Cancer ? No Cancer x1

8. Main approaches to multiclass classification • Single machine: Learning algorithms able to deal with multiple classes (e.g., KNN, Naïve Bayes) • Combining the outputs of several binary classifiers: • One-vs-all: one classifier per-class • All-vs-all: one classifier per pair of classes

9. Multilabel classification • To what category belong these documents:

10. Multilabel classification • A function: • Given:

11. Conventions n X={xij} y ={yj} m xi a w Slide taken from I. Guyon. Feature and Model Selection. Machine Learning Summer School, Ile de Re, France, 2008.

12. Conventions |L| n Z ={Zj} X={xij} m xi a w Slide taken from I. Guyon. Feature and Model Selection. Machine Learning Summer School, Ile de Re, France, 2008.

13. Multi-label classification • Each instance can be associated to a set of labels instead of a single one • Specialized multilabel classification algorithms must be developed • How to deal with the multilabel classification problem?

14. (Text categorization is perhaps the dominant multilabel application)

15. Multilabel classifiers • Transformation methods: Transform the multilabel classification task into several single-label problems • Adaptation approaches: Modify learning algorithms to support multilabel classification problems

16. Transformation methods • Copy transformation. Transforms the multilabel instances into several single-label ones Original ML problem Transformed ML problem (unweighted) Transformed ML problem (weighted)

17. Transformation methods • Select transformation. Replaces the multilabel of each instance by a single one Max Min Rand Original ML problem Transformed ML problem Ignore approach

18. Transformation methods • Label power set. Considers each unique set of labels in the ML problem as a single class Original ML problem Transformed ML problem Pruning can be applied

19. Transformation methods • Binaryrelevance. Learns a differentclassifier per eachdifferentlabel. Eachclassifieriistrainedusingthewhole data set byconsideringexamples of classi as positive and examples of otherclasses (j≠i) as negative • Howlabels are assignedto new instances? Original ML problem Data sets generated by BR

20. Transformation methods • Ranking by pairwise comparison. Learns a different classifier per each pair of different labels. Original ML problem Data sets generated by BR

21. Algorithm adaptation techniques • Many variants, including • Decision trees • Boosting ensembles • Probabilistic generative models • KNN • Support vector machines

22. Algorithm adaptation techniques • MLkNN. For each test instance: • Retrieve the top-k nearest neighbors to each instance • Compute the frequency of occurrence of each label • Assign a probability to each label and select the labels for the test instance

23. Original feature set Subset of feature Generation Evaluation Validation Stopping criterion Selected subset of feature yes no Feature selection in multilabel classification • An (almost) unstudied topic = opportunities • Wrappers can be applied directly (define an objective function to optimize based on a multilabel classifier) process From M. Dash and H. Liu. http://www.comp.nus.edu.sg/~wongszec/group10.ppt

24. Feature selection in multilabel classification • An almost un-studied topic = opportunities • Existing filter methods transform the multilabel problem and apply standard filters for feature selection

25. Statistics • Label cardinality • Label density

26. Evaluation of multilabel learning • (New) conventions: Data set Labels Predictions of a ML classifier for instances in D

27. Evaluation of multilabel learning • Hamming loss: • Classification accuracy:

28. Evaluation of multilabel learning • Precision: • Recall:

29. Evaluation of multilabel learning • F1-measure

30. Suggested readings • G. Tsoumakas, I. Katakis,I. Vlahavas. Mining multi-label data.Data Mining and Knowledge Discovery Handbook, Part 6, O. Maimon, L. Rokach (Ed.), Springer, 2nd edition, pp. 667-685, 2010. • G. Tsoumakas, I. Katakis. Multi-label classification: an overview. International Journal of Data Warehousing, 3(3), 1—13, 2007. • M. Zhang, Z. Zhou. ML-kNN, A lazy learning approach to multi-label learning. Pattern recognition 40:2038—2048, 2007. • M. Boutell, J. Luo, X. Shen. C. Brown. Learning multi-label scene classification. Pattern recognition 37:1757—1771, 2004.