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.
Original language | English (US) |
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Pages (from-to) | 816-824 |
Number of pages | 9 |
Journal | Statistics and Probability Letters |
Volume | 80 |
Issue number | 9-10 |
DOIs | |
State | Published - 2010 |
Keywords
- Binning
- Clustering
- Discretization
- Supervised learning
ASJC Scopus subject areas
- Statistics and Probability
- Statistics, Probability and Uncertainty