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Histogram-based class separability measure

version 1.0.0.0 (33.5 KB) by Theodoros Giannakopoulos
The provided functions demonstrate a histogram-based measure for class separability, given the sampl

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Updated 18 Feb 2008

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The provided functions demonstrate a histogram-based measure for class separability, given the samples from two classes (binary classification problem). The proposed error classification estimation method is described in (B) and it is based on estimating the pdf of each class using histograms. The function that estimates the class seperability method is computeHistError(). Function theoreticalError() computes the theoretical error for two Gaussian distributed classes. Function testClassSeperability() calls the other two functions and displays the results for two Gaussian distributed functions. It has to be noted that computeHistError() can be used for any kind of class distribution, since it estimates the pdf of each class using the histogram method.

We can use computeHistError() in order to estimate the separabilty of a binary classification problem, given the training samples of the two classes.

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Example

In order to execute the demo, call the testClassSeperability():

testClassSeperability(10000,1.0, 1.0, 3.0, 2.0, 1);

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Theodoros Giannakopoulos
http:/www.di.uoa.gr/~tyiannak
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Cite As

Theodoros Giannakopoulos (2022). Histogram-based class separability measure (https://www.mathworks.com/matlabcentral/fileexchange/18791-histogram-based-class-separability-measure), MATLAB Central File Exchange. Retrieved .

MATLAB Release Compatibility
Created with R2007b
Compatible with any release
Platform Compatibility
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