BINER: Binary Search Based Efficient Regression

Abstract

Regression is the study of functional dependency of one numeric variable with respect to another. In this paper, we present a novel, efficient, binary search based regression algorithm having the advantage of low computational complexity. These desirable features make BINER a very attractive alternative to existing approaches. The algorithm is interesting because instead of directly predicting the value of response variable, it recursively narrows down the range in which the response variable lies. Our empirical experiments with several real world datasets show that our algorithm, outperforms current state of art approaches and is faster by an order of magnitude.