RSNNS: analyzeClassification – R documentation

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analyzeClassification

Converts continuous outputs to class labels

Description

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 Tübingen. http://www.ra.cs.uni-tuebingen.de/SNNS/welcome.html

Christoph Bergmeir [aut, cre, cph], José M. Benítez [ths], Andreas Zell [ctb] (Part of original SNNS development team), Niels Mache [ctb] (Part of original SNNS development team), Günter Mamier [ctb] (Part of original SNNS development team), Michael Vogt [ctb] (Part of original SNNS development team), Sven Döring [ctb] (Part of original SNNS development team), Ralf Hübner [ctb] (Part of original SNNS development team), Kai-Uwe Herrmann [ctb] (Part of original SNNS development team), Tobias Soyez [ctb] (Part of original SNNS development team), Michael Schmalzl [ctb] (Part of original SNNS development team), Tilman Sommer [ctb] (Part of original SNNS development team), Artemis Hatzigeorgiou [ctb] (Part of original SNNS development team), Dietmar Posselt [ctb] (Part of original SNNS development team), Tobias Schreiner [ctb] (Part of original SNNS development team), Bernward Kett [ctb] (Part of original SNNS development team), Martin Reczko [ctb] (Part of original SNNS external contributors), Martin Riedmiller [ctb] (Part of original SNNS external contributors), Mark Seemann [ctb] (Part of original SNNS external contributors), Marcus Ritt [ctb] (Part of original SNNS external contributors), Jamie DeCoster [ctb] (Part of original SNNS external contributors), Jochen Biedermann [ctb] (Part of original SNNS external contributors), Joachim Danz [ctb] (Part of original SNNS development team), Christian Wehrfritz [ctb] (Part of original SNNS development team), Patrick Kursawe [ctb] (Contributors to SNNS Version 4.3), Andre El-Ama [ctb] (Contributors to SNNS Version 4.3)

Initial release

2019-09-16