Discretization, Clustering, Binning, Supervised Learning
Computer and Systems Architecture | Computer Engineering
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.
Gupta, Ankit; Mehrotra, Kishan; and Mohan, Chilukuri K., "A Clustering based Discretization for Supervised Learning" (2009). Electrical Engineering and Computer Science. Paper 3.