New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data
Machine Learning
2017-01-04 v1 Machine Learning
Abstract
In this paper, prediction for linear systems with missing information is investigated. New methods are introduced to improve the Mean Squared Error (MSE) on the test set in comparison to state-of-the-art methods, through appropriate tuning of Bias-Variance trade-off. First, the use of proposed Soft Weighted Prediction (SWP) algorithm and its efficacy are depicted and compared to previous works for non-missing scenarios. The algorithm is then modified and optimized for missing scenarios. It is shown that controlled over-fitting by suggested algorithms will improve prediction accuracy in various cases. Simulation results approve our heuristics in enhancing the prediction accuracy.
Cite
@article{arxiv.1701.00677,
title = {New Methods of Enhancing Prediction Accuracy in Linear Models with Missing Data},
author = {Mohammad Amin Fakharian and Ashkan Esmaeili and Farokh Marvasti},
journal= {arXiv preprint arXiv:1701.00677},
year = {2017}
}