analyzeClassification

0th

Percentile

Converts continuous outputs to class labels

This function converts the continuous outputs to binary outputs that can be used for classification. The two methods 402040, and winner-takes-all (WTA), are implemented as described in the SNNS User Manual 4.2.

Usage
analyzeClassification(y, method = "WTA", l = 0, h = 0)
Arguments
y

inputs

method

"WTA" or "402040"

l

lower bound, e.g. in 402040: l=0.4

h

upper bound, e.g. in 402040: h=0.6

Details

The following text is an edited citation from the SNNS User Manual 4.2 (pp 269):

402040

A pattern is recognized as classified correctly, if (i) the output of exactly one output unit is >= h (ii) the teaching output of this unit is the maximum teaching output (> 0) of the pattern (iii) the output of all other output units is <= l.

A pattern is recognized as classified incorrectly, if (i) and (iii) hold as above, but for (ii) holds that the teaching output is not the maximum teaching output of the pattern or there is no teaching output > 0.

A pattern is recognized as unclassified in all other cases.

The method derives its name from the commonly used default values l = 0.4, h = 0.6.

WTA

A pattern is recognized as classified correctly, if (i) there is an output unit with the value greater than the output value of all other output units (this output value is supposed to be a) (ii) a > h (iii) the teaching output of this unit is the maximum teaching output of the pattern (> 0) (iv) the output of all other units is < a - l.

A pattern is recognized as classified incorrectly, if (i), (ii), and (iv) hold as above, but for (iii) holds that the teaching output of this unit is not the maximum teaching output of the pattern or there is no teaching output > 0.

A pattern is recognized as unclassified in all other cases.

Commonly used default values for this method are: l = 0.0, h = 0.0.

Value

the position of the winning unit (i.e., the winning class), or zero, if no classification was done.

References

Zell, A. et al. (1998), 'SNNS Stuttgart Neural Network Simulator User Manual, Version 4.2', IPVR, University of Stuttgart and WSI, University of T<U+00FC>bingen. http://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html

encodeClassLabels