Many machine learning algorithms require the features to be categorical. Hence, they require all numeric-valued data to be discretized into intervals. In this paper, we present a new discretization method based on the receiver operating characteristics (ROC) Curve (AUC) measure. Maximum area under ROC curve-based discretization (MAD) is a global, static and supervised discretization method. MAD...

Bayesian Network (BN) is a classification technique widely used in Artificial Intelligence. Its structure is a Direct Acyclic Graph (DAG) used to model the association of categorical variables. However, in cases where the variables are numerical, a previous discretization is necessary. Discretization methods are usually based on a statistical approach using the data distribution, such as divisi...

In this paper, we present a method for color reduction of Persian carpet cartoons that increases both speed and accuracy of editing. Carpet cartoons are in two categories: machine-printed and hand-drawn. Hand-drawn cartoons are divided into two groups: before and after discretization. The purpose of this study is color reduction of hand-drawn cartoons before discretization. The proposed algorit...

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 ...

We present an efficient preconditioner for the orbital minimization method when the Hamiltonian is discretized using planewaves (i.e., pseudospectral method). This novel preconditioner is based on an approximate Fermi operator projection by pole expansion, combined with the sparsifying preconditioner to efficiently evaluate the pole expansion for a wide range of Hamiltonian operators. Numerical...

in this paper, the reduced dierential transform method is investigated fora nonlinear partial dierential equation modeling nematic liquid crystals, itis called the hunter-saxton equation. the main advantage of this methodis that it can be applied directly to nonlinear dierential equations withoutrequiring linearization, discretization, or perturbation. it is a semi analytical-numerical metho...

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 work reports the results of the application of the second-order maccormack method for numerical solution of‎ the conservative form of two-dimensional non-hydrostatic and fully compressible navier-stokes equations governing an inviscid and adiabatic atmosphere‎. various aspects of the computational approach such as discretization of the governing equations for the interior and boundary poin...

Most machine learning and data mining algorithms require that the training data contain only discrete attributes, which makes it necessary to discretize continuous numeric attributes. Bottom-up discretization algorithms are well-known methods. They mainly focus on discretizing data based on either local or global independence measure. In this paper, we present a novel bottom-up discretization m...

The solutions of the radiosity method are highly dependent on the discretization used . All methods used to generate these discretizations have to date depended upon the scene being formed of polygonal faces. However, these are often not the most efficient representations of the objects. The meshing process usually only takes geometry into account, making shadow edges awkward to deal with . In ...