Class IterativeRelief_Devel
source code
FeaturewiseDatasetMeasure that performs multivariate I-RELIEF
algorithm. Batch version allowing various kernels.
UNDER DEVELOPEMNT.
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=None,
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=None,
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.
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