frlearn.neighbours.classifiers
.FRONEC¶
-
class
frlearn.neighbours.classifiers.
FRONEC
(Q_type: int = 2, R_d_type: int = 1, k: int = 20, owa_weights: frlearn.utils.owa_operators.OWAOperator = <frlearn.utils.owa_operators.additive object>, nn_search: frlearn.neighbours.neighbour_search.NNSearch = <frlearn.neighbours.neighbour_search.KDTree object>)[source]¶ Implementation of the Fuzzy ROugh NEighbourhood Consensus (FRONEC) multilabel classifier.
- Parameters
- Q_typeint {1, 2, 3, }, default=2
Quality measure to use for identifying most relevant instances. Q^1 uses lower approximation, Q^2 uses upper approximation, Q^3 is the mean of Q^1 and Q^2.
- R_d_typeint {1, 2, }, default=1
Label similarity relation to use. R_d^1 is simple Hamming similarity. R_d^2 is similar, but takes the prior label probabilities into account.
- kint, default=20
Number of neighbours to consider for neighbourhood consensus.
- owa_weights: OWAOperator, default=additive()
OWA weights to use for calculation of soft maximum and/or minimum.
- nn_searchNNSearch, default=KDTree()
Nearest neighbour search algorithm to use.
References
-
__init__
(self, Q_type: 'int' = 2, R_d_type: 'int' = 1, k: 'int' = 20, owa_weights: 'OWAOperator' = <frlearn.utils.owa_operators.additive object at 0x7f8b1a153150>, nn_search: 'NNSearch' = <frlearn.neighbours.neighbour_search.KDTree object at 0x7f8b1a153110>)[source]¶ Initialize self. See help(type(self)) for accurate signature.