Sentence examples for multivariate discretization methods from inspiring English sources

ICA [22], Cluster-Ent-MDLP [23], HDD [3], and Cluster-RS-Disc [24] are examples of multivariate discretization methods.

Instead of applying discretization to individual supporting tracks (univariate discretization), multi-variate discretization methods that take into account of the interaction among features could be used to capture the missing patterns from univariate approaches (Bay, 2001).

Although several multivariate discretization algorithms overcome the drawback of univariate discretization methods by considering interactions among attributes, there are some weaknesses.

Based on BANJO's scorings across the different data discretization methods, the highest scores are obtained when considering the quantile ternary (Q3) discretized data.

The categories of discretization methods of 13 discretization algorithms.

Recent studies have focused on multivariate discretization that considers relations among attributes.

Despite the reported improvement, some multivariate discretization algorithms such as EMD [31] do not concentrate the natural group of data.

In order to discretize the attribute (A_1), the data views of discretization algorithms are different depending on discretization methods.

Although various discretization methods have been proposed to deal with this problem, discretization can result in information loss.

The spectral methods are essentially discretization methods for the approximate solution of partial differential equations.

This fact implies that the discretization method is consistent with the total population dynamics given by the continuous-time model, property not satisfied by all discretization methods [20].