The linear system with missing information is

investigated in this paper. New methods are

introduced to improve the Mean Squared Error (MSE)

on the test set in comparison to state-of-the-art method

s, through appropriate tuning of Bias-Variance

trade-off. The concept is to cluster the data and

adapt the learning model to each cluster. Hence,

we set forth a controlled bias into the problem and

positively utilize it to enhance learning capability on

the instances considered in some specific

neighborhood. To deal with missing infrormation,

we propose a novel algorithm “Missing-SCOP” based

on SCOP-KMEANS algorithm introduced by Wagstaff,

et al., utilizing the missing pattern of the dataset for

construction of a soft-constraint matrix and clustering

in missing scenario. It is shown that controlled

over-fitting suggested by our algorithm improves

prediction accuracy in various cases.

Numerical experiments approve the efficacy of our

proposed algorithm in enhancing the prediction

accuracy.