Document Type
Report
Date
11-2-2009
Keywords
Discretization, Clustering, Binning, Supervised Learning
Language
English
Disciplines
Computer and Systems Architecture | Computer Engineering
Description/Abstract
We address the problem of discretization of continuous variables for machine learning classification algorithms. Existing procedures do not use interdependence between the variables towards this goal. Our proposed method uses clustering to exploit such interdependence. Numerical results show that this improves the classification performance in almost all cases. Even if an existing algorithm can successfully operate with continuous variables, better performance is obtained if variables are first discretized. An additional advantage of discretization is that it reduces the overall time-complexity.
Recommended Citation
Gupta, Ankit; Mehrotra, Kishan; and Mohan, Chilukuri K., "A Clustering based Discretization for Supervised Learning" (2009). Electrical Engineering and Computer Science - All Scholarship. 3.
https://surface.syr.edu/eecs/3
Source
local input
Additional Information
SYR-EECS-2009-03