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Abstract

This research investigated how dimensional data could be downsized using multi-layers feature selection method. A combined Linear SVM Weight DQG5HOLHI)ZDs a proposed technique for data classification with classifier QDPHO\6XSSRUW9HFWRU0DFKLQHV690V7ZRGDWDVHWVLQFOXGLQJ65%&7DQG usps, were used for the experiment. We discovered that the proposed technique was more efficient than using either LineaU690:HLJKWRU5HOLHI)

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alone for dimensionality reduction. The dimensional data could be downsized from 2,308 to 8 attributes where the accuracy rate could reach 100 percent in SRBCT. The experimental result of SBRCT was also consistent with that of usps in which the dimensional data could be downsized 256 to 55 attributes with the accuracy of 95.76 percent.

Keywords : Dimensionality Reduction, Multi-/D\HUV)HDWXUH6HOHFWLRQ5HOLHI) Linear SVM Weight

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Algorithm 1 Feature Ranking Based on Linear SVM Weights

Input : Training sets, (Xi, Yi), i =1,…..m.

Output : Sorted feature ranking list.

1. Use grid search to find the best parameter C.

2. Train aL2-loss linear SVM model using the best C.

3. Sort the features according to the absolute values

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Algorithm ReliefF

Input: for each training instance a vector of attribute values and the class value

Output: the vector W of estimations of the qualities of attributes 1. set all weights W[A] :=0.0;

2. for i:=1 to ݉ do begin

3. randomly select an instance ܴ௜; 4. find k nearest hits ܪ௝ ;

5. for each class ܥ ് ݈ܿܽݏݏ(ܴ௜) do 6. from class ^ܥ find k nearest misses ^ܯ௝(ܥ) ; 7. for A :=1 to a do

8. ^ݓ[ܣ]ؔ ݓ[ܣ]െ σ^௞_௝ୀଵ^{ௗ௜௙௙൫஺,ோ}_(௠.௞)^{೔,ுೕ൯}

9. end;

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