Adjust feature ranking scores using correlation factor
correlationWeightedScore is a function used in code generated
by Diagnostic Feature
weights the original ranking scores in
idx] = correlationWeightedScore(
Z for the features in
X according to the correlation between features. Correlation
weighting reduces feature redundancy.
the score of a feature that has a high correlation to a higher ranking feature. The
correlation importance factor
alpha determines how much impact the
correlation level has on the feature ranking score.
Code that is generated by Diagnostic Feature
correlationWeightedScore when ranking features if
the specified correlation importance factor is greater than zero.
X — Feature set
vector | matrix
Feature set, specified as an m-by-1 vector or an m-by-n matrix, where m is the number of data samples and n is the number of features. For an ensemble-based feature set, m is the number of members in the ensemble.
Z — Original ranking scores
Original ranking scores, computed by a ranking method such as
bhattacharyyaDistance, and specified as a vector of length
n, where n is the number of features. The length
Z must be the same as the width of
alpha — Correlation importance factor
scalar in the range [0 1]
Correlation importance factor that determines how much impact correlation has on scores.
alphais set to
0, correlation has no impact on the ranking score.
alphais set to
1, correlation has the maximum possible impact on ranking score.
score — Adjusted ranking scores
Adjusted ranking scores, returned as a vector that is the same size as
idx — Updated ranking order
Updated ranking order after the scores are adjusted by correlation weighting, returned as a vector of integers.
 Theodoridis, Sergios, and Konstantinos Koutroumbas. Pattern Recognition, 182–183. 2nd ed. Amsterdam; Boston: Academic Press, 2003.
Introduced in R2020a