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Presenter : Yu-Ting LU Authors : Harun Ug˘uz 2011.KBS

A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm. Presenter : Yu-Ting LU Authors : Harun Ug˘uz 2011.KBS. Outlines. Motivation Objectives Methodology Experiments Conclusions Comments. Motivation.

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Presenter : Yu-Ting LU Authors : Harun Ug˘uz 2011.KBS

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  1. A two-stage feature selection method for text categorization by using information gain, principal component analysis and genetic algorithm Presenter : Yu-Ting LUAuthors : HarunUg˘uz2011.KBS

  2. Outlines • Motivation • Objectives • Methodology • Experiments • Conclusions • Comments

  3. Motivation • A major problem of text categorization is its large number of features. • Most of those are irrelevant noise that can mislead the classifier.

  4. Objectives • Two-stage feature selection and feature extraction is used to improve the performance of text categorization.

  5. Methodology

  6. Methodology – pre-processing • removing of stop-words • Stemming • term weighting • pruning of the words a, an, and, because, can, do, every, the… computer,computing,computation, computescomput Terms of the document collection prune the words that appear less thantwo times in the documents. documents

  7. Methodology – feature ranking with information gain • each term within the text is ranked depending on their importance for the classification in decreasing order using the IG method.

  8. Methodology – dimension reduction methods • principal component analysis • Genetic algorithm for feature selection p ≦ m 11011 00110 01110 11110 Individual’s encoding Fitness function Selection Mutation Crossover

  9. Methodology – text categorization methods • KNN classifier • C4.5 decision tree classifier

  10. Methodology – evaluation of the performance

  11. Experiments – datasets • Reuters dataset-21578 • Classic3 dataset

  12. Experiments – Reuters-21578 A document-term matrix is acquired with a dimension of 8158 × 7542 at the end of pre-processing.

  13. Experiments – Reuters-21578

  14. Experiments – Classic3 A document-term matrix is acquired in the dimension of 3891 × 6679 at the end of pre-processing.

  15. Experiments – Classic3

  16. Conclusions • The success of text categorization performed through the C4.5 decision tree and KNN algorithms using fewer features selected via IG-PCA and IG- GA is higher than the success acquired using features selected via IG. • Two-stage feature selection methods can improve the performance of text categorization.

  17. Comments • Advantages - understand the basic methods • Applications - text categorization

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