Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1152693 | Statistics & Probability Letters | 2010 | 9 Pages |
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 the variables are first discretized. An additional advantage of discretization is that it reduces the overall computation time.
Related Topics
Physical Sciences and Engineering
Mathematics
Statistics and Probability
Authors
Ankit Gupta, Kishan G. Mehrotra, Chilukuri Mohan,