PAGER: Parameterless, Accurate, Generic, Efficient kNN-based Regression.

Abstract

The problem of regression is to estimate the value of a dependent numeric variable based on the values of one or more independent variables. Regression algorithms are used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Although this problem has been studied extensively, most of these approaches are not generic in that they require the user to make an intelligent guess about the form of the regression equa- tion. In this paper we present a new regression algorithm PAGER { Parameterless, Accurate, Generic, Efficient kNN-based Regression. PAGER is also simple and outlier-resilient. These desirable features make PAGER a very attractive alternative to existing approaches. Our experimental study compares PAGER with 12 other algorithms on 4 standard real datasets, and shows that PAGER is more accurate than its competitors.