Information in Missing Patterns: Enhancing Prediction Accuracy in Weighted Linear Regression with Missing Data Using Soft Clustering

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posted on 2020-04-28, 07:38 authored by Ashkan EsmaeiliAshkan Esmaeili, Mohammadamin Fakharian, Yasaman Amiri Abyaneh

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.

History

Sharif University of Technology