Table 1.

Computing feature supports using Random KNN bidirectional voting

/* Generate n KNN classifiers using m features and compute accuracy acc for each KNN */

/* Return support for each feature */

p ← number of features in the data set;

m ← number of features for each KNN;

r ← number of KNN classifiers;

Fi ← feature list for ith KNN classifier;

C ← build r KNNs using m feature for each;

Perform query from base data sets using each KNN;

Compare predicted values with observed values;

Calculate accuracy, acc, for each base KNN;

$F\leftarrow {\bigcup }_{i=1}^r{F}_i$; {F is the list of features that appeared in r KNN classifiers};

for each f ∈ F do

C(f) ← list of KNN classifiers that used f;

$support\left(f\right)\leftarrow \frac{1}{\left|C\left(f\right)\right|}{\sum }_{knn\in C\left(f\right)}acc\left(knn\right);$

end for