frlearn.neighbours.classifiers
.FRNN¶
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class
frlearn.neighbours.classifiers.
FRNN
(*, upper_weights: frlearn.utils.owa_operators.OWAOperator = <frlearn.utils.owa_operators.additive object>, upper_k: int = 20, lower_weights: frlearn.utils.owa_operators.OWAOperator = <frlearn.utils.owa_operators.additive object>, lower_k: int = 20, nn_search: frlearn.neighbours.neighbour_search.NNSearch = <frlearn.neighbours.neighbour_search.KDTree object>)[source]¶ Implementation of Fuzzy Rough Nearest Neighbour (FRNN) classification (FRNN).
- Parameters
- upper_weightsOWAOperator, default=additive()
OWA weights to use in calculation of upper approximation of decision classes.
- upper_kint, default = 20
Effective length of upper weights vector (number of nearest neighbours to consider).
- lower_weightsOWAOperator, default=additive()
OWA weights to use in calculation of lower approximation of decision classes.
- lower_kint, default = 20
Effective length of lower weights vector (number of nearest neighbours to consider).
- nn_searchNNSearch, default=KDTree()
Nearest neighbour search algorithm to use.
Notes
With strict upper_weights and lower_weights, this is FRNN classification as presented in [R1fba870e1728-1]. The use of OWA operators for the calculation of fuzzy rough sets was proposed in [R1fba870e1728-2], and OWA operators were first explicitly combined with FRNN in [R1fba870e1728-3].
References
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__init__
(self, *, upper_weights: 'OWAOperator' = <frlearn.utils.owa_operators.additive object at 0x7f8b1a501ed0>, upper_k: 'int' = 20, lower_weights: 'OWAOperator' = <frlearn.utils.owa_operators.additive object at 0x7f8b1a501fd0>, lower_k: 'int' = 20, nn_search: 'NNSearch' = <frlearn.neighbours.neighbour_search.KDTree object at 0x7f8b1a5085d0>)[source]¶ Initialize self. See help(type(self)) for accurate signature.
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class
Model
(classifier, X, y)¶