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Information in Missing Patterns: Enhancing Prediction Accuracy in Weighted Linear Regression with Missing Data Using Soft Clustering
  • Ashkan Esmaeili ,
  • Mohammadamin Fakharian ,
  • Yasaman Amiri Abyaneh
Ashkan Esmaeili
Sharif University of Technology

Corresponding Author:[email protected]

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Mohammadamin Fakharian
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Yasaman Amiri Abyaneh
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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