Authors:
Harshit Dubey
;
Saket Bharambe
and
Vikram Pudi
Affiliation:
International Institute of Information Technology - Hyderabad, India
Keyword(s):
Regression, Gaussian, Prediction, Logarithmic Performance;, Linear Performance, Binary Search.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Clustering and Classification Methods
;
Computational Intelligence
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Methodologies and Technologies
;
Operational Research
;
Optimization
;
Pre-Processing and Post-Processing for Data Mining
;
Soft Computing
;
Symbolic Systems
;
Web Mining
Abstract:
Regression is the study of functional dependency of one variable with respect to other variables. In this paper we propose a novel regression algorithm, BINGR, for predicting dependent variable, having the advantage of low computational complexity. The algorithm is interesting because instead of directly predicting the value of the response variable, it recursively narrows down the range in which response variable lies. BINGR reduces the computation order to logarithmic which is much better than that of existing standard algorithms. As BINGR is parameterless, it can be employed by any naive user. Our experimental study shows that our technique is as accurate as the state of the art, and faster by an order of magnitude.