About: The Dominance-based Rough Set Approach (DRSA) is an innovative preference learning approach. It takes as input a set of objects (learning set) described with respect to a collection of condition and decision attributes. It generates a set of if-then decision rules. Initial versions of dominance based rough set approximation methods assume a single decision maker. Furthermore, the proposed extensions to group decision making mainly use an input oriented aggregation strategy, which requires a high level of agreement between the decision makers. In this paper, we propose an output oriented aggregation strategy to coherently combine different sets of decision rules obtained from different decision makers. The proposed aggregation algorithm is illustrated by using real-world data relative to a business school admission where two decision makers are involved. Results show that aggregation algorithm is able to reproduce the individual assignments of students with a very limited preferential information loss.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : covidontheweb.inria.fr associated with source document(s)

AttributesValues
type
value
  • The Dominance-based Rough Set Approach (DRSA) is an innovative preference learning approach. It takes as input a set of objects (learning set) described with respect to a collection of condition and decision attributes. It generates a set of if-then decision rules. Initial versions of dominance based rough set approximation methods assume a single decision maker. Furthermore, the proposed extensions to group decision making mainly use an input oriented aggregation strategy, which requires a high level of agreement between the decision makers. In this paper, we propose an output oriented aggregation strategy to coherently combine different sets of decision rules obtained from different decision makers. The proposed aggregation algorithm is illustrated by using real-world data relative to a business school admission where two decision makers are involved. Results show that aggregation algorithm is able to reproduce the individual assignments of students with a very limited preferential information loss.
Subject
  • Analysis
  • Prediction
  • Decision-making
  • Neuropsychological assessment
  • Unsolved problems in neuroscience
  • Theoretical computer science
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software