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.
|
|
__init__(self,
threshold=1.0e-2,
kernel=None,
kernel_width=1.0,
w_guess=None,
**kwargs)
Constructor of the IRELIEF class. |
source code
|
|
|
|
|
|
|
|
|
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__,
__format__,
__hash__,
__reduce__,
__reduce_ex__,
__sizeof__,
__subclasshook__
|
|
Inherited from object:
__class__
|
__init__(self,
threshold=1.0e-2,
kernel=None,
kernel_width=1.0,
w_guess=None,
**kwargs)
(Constructor)
| source code
|
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__
|
|
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.
|