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

Algorithm 1

The proposed supervised 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)	Randomly select and label K0 samples;
(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-QBC: compute in (1) and E[I(xn)] in (6);
(10)	min-max normalization of and E[I(xn)], marked as and
(11)	Compute
(12)	EGO-EMCM: compute (xn) in (2) and E[I(xn)] in (6);
(13)	min-max normalization of (xn) and E[I(xn)], marked as (xn)^∗ and
(14)	Compute
(15)	end
(16)	Label the sample with the largest Tn and add it to the training set.
(17)	end
(18)	Update the regression model f(x) with the labeled K samples.