[Retracted] Active Learning Query Strategies for Linear Regression Based on Efficient Global Optimization

Algorithm 2

The proposed unsupervised EGO-ALR approach.

(1)	Input: xn, a pool of N unlabeled samples; K, maximum number of samples to label;
(2)	c, weighting parameters.
(3)	Output: regression model f(x)
(4)	Select and label the initial K0 samples with the GSx (or RD) algorithm;
(5)	Construct the initial regression model f(x) with K0 samples;
(6)	for m = K0  + 1, …, K do
(7)	Build L regression models using bootstrap from the training set
(8)	for n = m, …, K do
(9)	EGO-GSx: compute dn in (4) and E[I(xn)] in (6);
(10)	min-max normalization of dn and E[I(xn)], marked as and
(11)	Compute
(12)	RD-EGO: perform k-means (k = n) clustering on all samples in the pool;
(13)	Identify the largest cluster that does not contain labeled samples
(14)	Compute E[I(xn)] in (6) for the samples in the cluster
(15)	end
(16)	Label the sample with the largest Tn (or E[I(xn)]) and add it to the training set.
(17)	end
(18)	Update the regression model f(x) with the labeled K samples.