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

Aditya Desai, Himanshu Singh, Vikram Pudi

2010

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.

Paper Citation

in Harvard Style

Desai A., Singh H. and Pudi V. (2010). SEAR - Scalable, Efficient, Accurate, Robust kNN-based Regression . In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010) ISBN 978-989-8425-28-7, pages 392-395. DOI: 10.5220/0003068703920395

in Bibtex Style

@conference{kdir10,
author={Aditya Desai and Himanshu Singh and Vikram Pudi},
title={SEAR - Scalable, Efficient, Accurate, Robust kNN-based Regression},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)},
year={2010},
pages={392-395},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003068703920395},
isbn={978-989-8425-28-7},
}

in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2010)
TI - SEAR - Scalable, Efficient, Accurate, Robust kNN-based Regression
SN - 978-989-8425-28-7
AU - Desai A.
AU - Singh H.
AU - Pudi V.
PY - 2010
SP - 392
EP - 395
DO - 10.5220/0003068703920395