# 使用Matlab进行特征选择

(1)序列前向选择( SFS , Sequential Forward Selection )

(2)序列后向选择( SBS , Sequential Backward Selection )

(3) 双向搜索( BDS , Bidirectional Search )

（4）序列浮动选择( Sequential Floating Selection )

<1>序列浮动前向选择( SFFS , Sequential Floating Forward Selection )

<2>序列浮动后向选择( SFBS , Sequential Floating Backward Selection )

算法评价：序列浮动选择结合了序列前向选择、序列后向选择、增L去R选择的特点，并弥补了它们的缺点。

## Feature Selection using Matlab

The DEMO includes 5 feature selection algorithms:

• Sequential Forward Selection (SFS)

• Sequential Floating Forward Selection (SFFS)

• Sequential Backward Selection (SBS)

• Sequential Floating Backward Selection (SFBS)

• ReliefF

Two CCR estimation methods:

• Cross-validation

• Resubstitution

After selecting the best feature subset, the classifier obtained can be used for classifying any pattern.

Figure: Upper panel is the pattern x feature matrix

Lower panel left are the features selected

Lower panel right is the CCR curve during feature selection steps

Right panel is the classification results of some patterns.

This software was developed using Matlab 7.5 and Windows XP.

AIIA Lab, Thessaloniki, Greece,

jimver@aiia.csd.auth.gr

costas@aiia.csd.auth.gr

In order to run the DEMO:

In order to run the demo:

- A PC with Windows XP is needed.

- Use Matlab7.5 or later to run DEMO.m

1) Select the ‘finalvec.mat’ dataset (patterns x [features+1] matrix) from 'PatTargMatrices' folder. The last column of ‘finalvec.mat’ are the targets.

2) Press the run button on the panel. It is the second one.

3) After the selection of the optimum feature set, select a set of patterns for classification using the open folder button (last button). It can be the same data-set that was used for training the feature selection algorithm

% REFERENCES:

[1] D. Ververidis and C. Kotropoulos, "Fast and accurate feature subset selection applied into speech emotion recognition," Els. Signal Process., vol. 88, issue 12, pp. 2956-2970, 2008.

[2] D. Ververidis and C. Kotropoulos, "Information loss of the Mahalanobis distance in high dimensions: Application to feature selection," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 12, pp. 2275-2281, 2009.

Version_5.zip (3.14MB)