Displaying similar documents to “Generalized Discernibility Function Based Attribute Reduction in Incomplete Decision Systems”

An incremental approach to obtaining attribute reduction for dynamic decision systems

Liu Wenjun (2016)

Open Mathematics

Similarity:

In the 1960s Professor Hu Guoding proposed a method of measuring information based on the idea that connotation and denotation of a concept satisfies inverse ratio rule. According to this information measure, firstly we put forward the information quantity for information systems and decision systems; then, we discuss the updating mechanism of information quantity for decision systems; finally, we give an attribute reduction algorithm for decision tables with dynamically varying attribute...

Minimal decision rules based on the apriori algorithm

María Fernández, Ernestina Menasalvas, Óscar Marbán, José Peña, Socorro Millán (2001)

International Journal of Applied Mathematics and Computer Science

Similarity:

Based on rough set theory many algorithms for rules extraction from data have been proposed. Decision rules can be obtained directly from a database. Some condition values may be unnecessary in a decision rule produced directly from the database. Such values can then be eliminated to create a more comprehensible (minimal) rule. Most of the algorithms that have been proposed to calculate minimal rules are based on rough set theory or machine learning. In our approach, in a post-processing...

A rough set-based knowledge discovery process

Ning Zhong, Andrzej Skowron (2001)

International Journal of Applied Mathematics and Computer Science

Similarity:

The knowledge discovery from real-life databases is a multi-phase process consisting of numerous steps, including attribute selection, discretization of real-valued attributes, and rule induction. In the paper, we discuss a rule discovery process that is based on rough set theory. The core of the process is a soft hybrid induction system called the Generalized Distribution Table and Rough Set System (GDT-RS) for discovering classification rules from databases with uncertain and incomplete...

Multi-attribute evaluation with imprecise vector utility

Sixto Ríos-Insua, Alfonso Mateos (1996)

Revista de la Real Academia de Ciencias Exactas Físicas y Naturales

Similarity:

We consider the multi-attribute decision making problem with incomplete information on the decision maker's preferences, given by an imprecise vector utility function. We introduce an approximation set to the utility efficient set which may be used to aid a decision maker in reaching a final compromise strategy. We provide sorne properties and an interactive procedure based on such approximation set.

A model of decision with linguistic knowledge.

María Teresa Lamata Jiménez (1994)

Mathware and Soft Computing

Similarity:

The aim of this paper is to develop a new aggregating method for the decision problem in which the possible values of rewards are known in linguistic terms. We show new operators for solving this problem, as well as the way in which OWA operators provide us with an adequate framework for representing the optimism degree of the decision maker in case we have no information about the real state.