Discretization of continuous attributes is an important task in rough sets and many discretization algorithms have been proposed. However, most of the current discretization algorithms are univariate, which may reduce the classification ability of a given decision table. To solve this problem, we propose a supervised and multivariate discretization algorithm — SMDNS in rough sets, which is deri...

Biometric discretization extracts a binary string from a set of real-valued features per user. This representative string can be used as a cryptographic key in many security applications upon error correction. Discretization performance should not degrade from the actual continuous features-based classification performance significantly. However, numerous discretization approaches based on inef...

In AI and machine learning techniques such as decision trees and Bayesian networks, there is a growing need for converting continuous data into discrete form. Several approaches are available for discretization, however finding an appropriate and efficient discretization method is a challenging task. In this paper, we present an impurity based dynamic multi-interval discretization approach for ...

in this paper, an iterative scheme for extracting approximate solutions of two dimensional volterra-fredholm integral equations is proposed. considering some conditions on the kernel of the integral equation obtained by discretization of the integral equation, the convergence of the approximate solution to the exact solution is investigated. several examples are provided to demonstrate the effc...

in this paper, a new numerical method for solving time-fractional diffusion equations is introduced. for this purpose, finite difference scheme for discretization in time and chebyshev collocation method is applied. also, to simplify application of the method, the matrix form of the suggested method is obtained. illustrative examples show that the proposed method is very efficient and accurate.

This work analyses the implications of discretization on fluid-fairness. In this context, discretization means being fluid-fair with a chosen granularity. The operation of a discretized scheduler is very simple and has constant overhead, and provides a parameterizable deviation from the optimal delay and fairness bounds provided by a fluid scheduler. Discretized schedulers can actually improve ...

Discretization is an important preprocessing technique in data mining tasks. Univariate Discretization is the most commonly used method. It discretizes only one single attribute of a dataset at a time, without considering the interaction information with other attributes. Since it is multi-attribute rather than one single attribute determines the targeted class attribute, the result of Univaria...

This paper describes a state space discretization scheme based on statistical experimental designs generated from orthogonal arrays of strength three with index unity. Chen et al. (1999) employed this eecient discretization scheme in a numerical solution method for high-dimensional continuous-state stochastic dynamic programming (SDP). These OAs are instrumental in reducing the dimensionality o...

Classification algorithms are used in numerous applications everyday, from assigning letter grades to student student’s scores, to computerized letter recognition in mail processing. Discretization consists of applying a set of rules to reduce the number of discrete intervals from which an attribute is assigned. Discretization is generally applied to datasets whose numerical range consists of c...

We analyze the spatial discretization errors associated with solutions of onedimensional hyperbolic conservation laws by discontinuous Galerkin methods in space. We show that the leading term of the spatial discretization error with piecewise polynomial approximations of degree p is proportional to a Radau polynomial of degree p+1 on each element. We also prove that the local and global discret...