FeaturewiseMeasure 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.
Notes
Available conditional attributes:
(Conditional attributes enabled by default suffixed with +)
References
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.
Constructor of the IRELIEF class.
Parameters : | enable_ca : None or list of str
disable_ca : None or list of str
null_dist : instance of distribution estimator
auto_train : bool
force_train : bool
space: str, optional :
postproc : Node instance, optional
descr : str
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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
Indicate that this measure doesn’t have to be trained
Exponential kernel.