The document proposes a novel approach for document and feature reduction in text categorization using prototypes and rough sets. It introduces a prototype-based algorithm to reduce documents while preserving classification accuracy. A rough set-based method is also presented to select a subset of relevant features. The methods are evaluated on benchmark datasets and are shown to improve both classification performance and computational efficiency compared to baseline methods.

1. A novel approach based on prototypes and rough sets for document and feature reductions in text categorization Shing-Hua Ho and Jung-Hsien Chiang Reporter :CHE-MIN LIAO 2007/8/27

10. Document reduction based on prototype concept Step 05: Determine the index of the closest prototype to each document dv as Iv =arg min( Dvt ) Step 06: If Iv = z , ∀ dv ∈ Gz Then go to Step 11 End If Step 07: If s ( PIv )≠ s ( Pz ) ∀ cv ∈ Gz Then Set Z = Z +1 and split Gz into two subgroups Ga and Gb Update their means: Pa =mean( Ga ) and Pb =mean( Gb ) If s ( Pa )= s ( Pb ) Then go to Step 04 End If End If

14. Feature reduction based on rough sets

15. Feature reduction based on rough sets

16. Feature reduction based on rough sets

17. Feature reduction based on rough sets

18. Feature reduction based on rough sets The rough-based feature selection algorithm achieves exclusive clusters and required to determine the desired number of clusters.Theoretically,the suitable maximum number of clusters is estimated as ,where N is the size of the features

19. Feature reduction based on rough sets

20. Performance evaluation and comparison

24. Experimental design and result analysis

25. Experimental design and result analysis

27. Experimental design and result analysis

28. Experimental design and result analysis