inputSelectSequential

Input selection via sequential forward selection

Contents

Syntax

Description

[bestSelectedInput, bestRecogRate, allSelectedInput, allRecogRate, elapsedTime]=inputSelectSequential(DS, inputNum, classifier, param, showPlot) performs input selection via sequential forward selection.

• Input:
  • DS: design set
  • inputNum: up to inputNum inputs are selected
  • classifier: classifier for input selection
  • param: parameters for classifier
  • showPlot: 0 for not plotting (default: 1)
• Output:
  • bestSelectedInput: overall selected input index
  • bestRecogRate: recognition rate based on the final selected input
  • allSelectedInput: all selected input during the process
  • allRecogRate: all recognition rate
  • elapseTime: elapsed time

Example

Use KNNC classifier for input selection

DS=prData('iris');
figure; inputSelectSequential(DS);

Construct 10 "knnc" models, each with up to 4 inputs selected from all 4 inputs...

Selecting input 1:
Model 1/10: selected={sepal length} => Recog. rate = 58.67%
Model 2/10: selected={sepal width} => Recog. rate = 48.00%
Model 3/10: selected={petal length} => Recog. rate = 88.00%
Model 4/10: selected={petal width} => Recog. rate = 88.00%
Currently selected inputs: petal length => Recog. rate = 88.0%

Selecting input 2:
Model 5/10: selected={petal length, sepal length} => Recog. rate = 90.67%
Model 6/10: selected={petal length, sepal width} => Recog. rate = 90.67%
Model 7/10: selected={petal length, petal width} => Recog. rate = 95.33%
Currently selected inputs: petal length, petal width => Recog. rate = 95.3%

Selecting input 3:
Model 8/10: selected={petal length, petal width, sepal length} => Recog. rate = 95.33%
Model 9/10: selected={petal length, petal width, sepal width} => Recog. rate = 95.33%
Currently selected inputs: petal length, petal width, sepal length => Recog. rate = 95.3%

Selecting input 4:
Model 10/10: selected={petal length, petal width, sepal length, sepal width} => Recog. rate = 96.00%
Currently selected inputs: petal length, petal width, sepal length, sepal width => Recog. rate = 96.0%

Overall maximal recognition rate = 96.0%.
Selected 4 inputs (out of 4): petal length, petal width, sepal length, sepal width

Use SVMC classifier for input selection

DS=prData('iris');
figure; inputSelectSequential(DS, inf, 'nbc');

Construct 10 "nbc" models, each with up to 4 inputs selected from all 4 inputs...

Selecting input 1:
Model 1/10: selected={sepal length} => Recog. rate = 72.67%
Model 2/10: selected={sepal width} => Recog. rate = 55.33%
Model 3/10: selected={petal length} => Recog. rate = 95.33%
Model 4/10: selected={petal width} => Recog. rate = 95.33%
Currently selected inputs: petal length => Recog. rate = 95.3%

Selecting input 2:
Model 5/10: selected={petal length, sepal length} => Recog. rate = 90.00%
Model 6/10: selected={petal length, sepal width} => Recog. rate = 91.33%
Model 7/10: selected={petal length, petal width} => Recog. rate = 96.00%
Currently selected inputs: petal length, petal width => Recog. rate = 96.0%

Selecting input 3:
Model 8/10: selected={petal length, petal width, sepal length} => Recog. rate = 96.00%
Model 9/10: selected={petal length, petal width, sepal width} => Recog. rate = 96.00%
Currently selected inputs: petal length, petal width, sepal length => Recog. rate = 96.0%

Selecting input 4:
Model 10/10: selected={petal length, petal width, sepal length, sepal width} => Recog. rate = 95.33%
Currently selected inputs: petal length, petal width, sepal length, sepal width => Recog. rate = 95.3%

Overall maximal recognition rate = 96.0%.
Selected 2 inputs (out of 4): petal length, petal width

Published with MATLAB® R2020b