Class IterativeReliefOnline_Devel
source code
FeaturewiseDatasetMeasure that performs multivariate I-RELIEF
algorithm. Online version.
UNDER DEVELOPMENT
Online version with complexity O(T*N*I),
where N is the number of instances and I the number of features.
See: Y. Sun, Iterative RELIEF for Feature Weighting: Algorithms,
Theories, and Applications, IEEE Trans. on Pattern Analysis and
Machine Intelligence (TPAMI), vol. 29, no. 6, pp. 1035-1051, June
2007. http://plaza.ufl.edu/sunyijun/Paper/PAMI_1.pdf
Note that this implementation is not fully online, since hit and
miss dictionaries (H,M) are computed once at the beginning using
full access to all labels. This can be easily corrected to a full
online implementation. But this is not mandatory now since the
major goal of this current online implementation is reduction of
computational complexity.
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__init__(self,
a=5.0,
permute=True,
max_iter=3,
**kwargs)
Constructor of the IRELIEF class. |
source code
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Inherited from IterativeRelief_Devel :
compute_M_H
Inherited from base.FeaturewiseDatasetMeasure :
__repr__ ,
combiner
Inherited from base.DatasetMeasure :
__call__ ,
null_dist ,
transformer ,
untrain
Inherited from misc.state.ClassWithCollections :
__getattribute__ ,
__new__ ,
__setattr__ ,
__str__ ,
reset
Inherited from object :
__delattr__ ,
__hash__ ,
__reduce__ ,
__reduce_ex__
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Inherited from object :
__class__
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