SEAR - Scalable, Efficient, Accurate, Robust kNN-based Regression

Abstract

Regression algorithms are used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Statistical approaches although popular, are not generic in that they require the user to make an intelligent guess about the form of the regression equation. In this paper we present a new regression algorithm SEAR – Scalable, Efficient, Accurate kNN-based Regression. In addition to this, SEAR is simple and outlier-resilient. These desirable features make SEAR a very attractive alternative to existing approaches. Our experimental study compares SEAR with fourteen other algorithms on five standard real datasets, and shows that SEAR is more accurate than all its competitors.

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Cite this paper

@inproceedings{Desai2010SEARS, title={SEAR - Scalable, Efficient, Accurate, Robust kNN-based Regression}, author={Aditya Desai and Himanshu Singh and Vikram Pudi}, booktitle={KDIR}, year={2010} }