Titlebook: Rough Sets and Current Trends in Computing; Second International Wojciech Ziarko,Yiyu Yao Conference proceedings 2001 Springer-Verlag Berli

SPECT

Rough Sets: Trends, Challenges, and Prospects of rough set methodology in the context of its likely impact on the future computing devices. The opinions presented are solely of the author and do not necessarily reflect the point of view of the majority of the rough set community.

间接

To What Extent Can Computers Aid Human Activity? Toward Second Phase Information Technologylogy that can back up the human activity is proposed. It must be an intelligent system to enable a computer-led interactive system. A conceptual architecture of the system and then various technologies to make up the architecture are discussed. The main issues are; a modeling scheme to accept and re

infringe

Rough Sets and Decision Algorithmserms of some attributes. Often we distinguish in the information system condition and decision attributes. Such information system is called a .. The decision table describes decisions in terms of conditions that must be satisfied in order to carry out the decision specified in the decision table. W

Mumble

使苦恼

灯泡

Dynamic System Visualization with Rough Performance Mapssualization of iterative chaos, performance maps are constructed using rule evaluation and require a minimum of à priori knowledge of the system under consideration. By the use of carefully selected performance evaluation rules combined with color-coding, they convey a wealth of information to the i

画布

obstinate

An Investigation of β-Reduct Selection within the Variable Precision Rough Sets Model to define satisfactory levels of quality of classification and β (confidence) value. This paper considers VPRS analysis when the DM only defines a satisfactory level of quality of classification. Two criteria for selecting a β-reduct under this condition are discussed. They include the use of permi

工作

Scalable Feature Selection Using Rough Set Theoryed on ., which is an alternative solution for the exhaustive approach. Our study shows that our algorithm improves the classical one from three points of view: computation time, reducts size and the accuracy of induced model.

ABYSS

Rough Set-Based Dimensionality Reduction for Multivariate Adaptive Regression Splineselatively high complexity. This limits their applicability to real world applications. Friedman’s Multivariate Adaptive Regression Splines (MARS) is a function approximator that produces continuous models of multi-dimensional functions using recursive partitioning and multidimensional spline curves