Class IterativeRelief
source code
FeaturewiseDatasetMeasure that performs multivariate I-RELIEF
algorithm. Batch version.
Batch I-RELIEF-2 feature weighting algorithm. Works for binary or
multiclass class-labels. Batch version with complexity O(T*N^2*I),
where T is the number of iterations, N the number of instances, 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 current implementation allows to use only
exponential-like kernels. Support for linear kernel will be
added later.
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__init__(self,
threshold=1.0e-2,
kernel_width=1.0,
w_guess=None,
**kwargs)
Constructor of the IRELIEF class. |
source code
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Inherited from base.FeaturewiseDatasetMeasure :
__repr__
Inherited from base.DatasetMeasure :
__call__ ,
null_dist
Inherited from misc.state.ClassWithCollections :
__getattribute__ ,
__new__ ,
__setattr__ ,
__str__ ,
reset
Inherited from object :
__delattr__ ,
__format__ ,
__hash__ ,
__reduce__ ,
__reduce_ex__ ,
__sizeof__ ,
__subclasshook__
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Inherited from object :
__class__
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__init__(self,
threshold=1.0e-2,
kernel_width=1.0,
w_guess=None,
**kwargs)
(Constructor)
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Constructor of the IRELIEF class.
- Parameters:
combiner - The combiner is only applied if the computed featurewise dataset
measure is more than one-dimensional. This is different from a
transformer , which is always applied. By default, the sum of
absolute values along the second axis is computed.
- Overrides:
object.__init__
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Compute hit/miss dictionaries.
For each instance compute the set of indices having the same
class label and different class label.
Note that this computation is independent of the number of
features.
XXX should it be some generic function since it doesn't use self
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