PERC

Percentile combining classifier

    W = PERC(V,P)
    W = V*PERC([],P)

Input
V	Set of classifiers
P	Percentile, 0 <= P <= 100

Output
W	Percentile combining classifier on V

Description

If V = [V1,V2,V3, ... ] is a set of classifiers trained on the  same classes and W is the percentile combiner: it selects the class  defined by the percentile of the outputs of the input classifiers. This  might also be used as A*[V1,V2,V3]*PERC([],P) in which A is a dataset to  be classified.

PERC([],0)	is equal to MINC
PERC([],50)	is equal to MEDIANC
PERC([],100)	is equal to MAXC

If it is desired to operate on posterior probabilities then the  input classifiers should be extended like V = V*CLASSC;

The base classifiers may be combined in a stacked way (operating  in the same feature space by V = [V1,V2,V3, ... ] or in a parallel  way (operating in different feature spaces) by V = [V1;V2;V3; ... ]

See also

mappings, datasets, votec, maxc, minc, meanc, medianc, prodc, averagec, stacked, parallel,

Example(s)