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PAGER: Parameterless, Accurate, Generic, Efficient kNN-based RegressionAuthors: Aditya Desai,Himanshu singh,Vikram Pudi Date: 2009-10-09 Report no: IIIT/TR/2009/157 AbstractThe problem of regression is to estimate the value of a dependent numeric variable based on the values of one or more independent numeric variables. Regression algorithms can be used for prediction (including forecasting of time-series data), inference, hypothesis testing, and modeling of causal relationships. Although this problem has been studied extensively in statistics, it has not received as much attention from the data mining community. Statistical approaches 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 PAGER -- Parameterless, Accurate, Generic, Efficient kNN-based Regression. In addition PAGER is simple and outlier-resilient. These desirable features make PAGER a very attractive alternative to existing approaches. Our experimental study compares PAGER with twelve other algorithms on four standard real datasets, and shows that PAGER is more accurate than all its competitors. Full report: pdf Centre for Data Engineering |
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