1. A Comparative Study on Featuree
Selection in Text Categorization
Presented by Hector Franco
TCD

2. objective
• Reduce the number of dimensions. Some
methods have problems with too high
dimension.

3. Statistical classification methods.
1. Regression models
2. Knn
3. Bayes
4. Decision treees
5. Neural netwoks
6. Symbolic rule learning
7. Inductive learning algorithms

4. Features:
• DF Document frequency thresholding
• IG Information Gain
• MI Mutual information
• CHI statistic
• TS Term strength

5. DF Document frequency thresholding
• Number of documents in which term occurs.
• It remove rare terms.

6. Information gain
• Of the term t:
• Time: O(N) space O(VN)
• N=Documents, V=vocabulary

7. Mutual information
• If t and c indpendent -> value 0.
O(VN)

8. Statistic (CHI)
• Measure of the lack of independence between t
and c,
• A t and c occurs, B t and not c
• C not t and c , D not t and not c
• N total number of documents
It t and c independent value =0.

9. Statistic (CHI)
O(VN)

10. Ts term strength
• Based on document clustering
• How common is a term is likely to appear in
closely related documents.
• O(N^2)

11. EXPERIMENTS
• Classifiers
– kNN
– LLSF
• Corporas:
– Reuters-22173
– OHSUMED
• Use of SMART system for unified
preprocessing.

12. Reduction on number of words
Have the best performance at 2000
vocabulary size
Best ig (more reduction)and chi

13. Most
aggressive in
term removal

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