Adaptive Local Hyperplane for Regression Tasks

Abstract

The paper introduces novel machine learning (data mining) algorithm called Adaptive Local Hyperplane (ALH) and it presents its application in solving regression problems. ALH algorithm has recently shown extremely good results in classification, and it is adopted for solving regression tasks here. It is a local margin maximizing algorithm in the original, weighted, input space blending a Nearest Neighbors (NN) based approaches and Support Vector Machines (SVMs) ideas about the maximal margin. In performing such a task it uses only K closest points to the query data point. Results for four benchmarking regression data sets show superior performance to SVMs as well as to the other established regression methods