We consider a decision problem where a group of individuals evaluates multiattribute alternatives. We explore the minimal required agreements that are sufficient to specify the group utility function. A surprising result is that, under some conditions, a bilateral agreement among pairs of individuals on a single attribute is sufficient to derive the multiattribute group utility. The bilateral a...

The paper presents a comparison between two feature selection methods; the Importance Score (IS) and a genetic algorithm-based (GA) method. The goal of both is to achieve better performing rules produced by the AQ15 learning system. The IS method performs a greedy-like search based on an attributional score that represents the importance of each attribute in classifying the decision classes. IS...

This study addresses swarm intelligence-based approaches in data quality detection. First, three typical swarm intelligence models and their applications in abnormity detection are introduced, including Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Bee Colony Optimization (BCO). Then, it presents three approaches based on ACO, PSO and BCO for detection of attribute outliers ...

In this paper we represent an efficient query optimization technique for the multi-valued rough relational database which follows the indiscernibility relation in its domain. This notion is perceived by using an encoding function to convert a multi-valued attribute to a constant single valued attribute. A simple select-querying technique is provided for selecting the tuples of single-valued att...

This paper critically analyzes reduct construction methods at two levels. At a high level, one can abstract commonalities from the existing algorithms, and classify them into three basic groups based on the underlying control structures. At a low level, by adopting different heuristics or fitness functions for attribute selection, one is able to derive most of the existing algorithms. The analy...

This study develops an identification procedure for general fuzzy measures using genetic algorithms. In view of the difficulty in data collection in practice, the amount of input data is simplified through a sampling procedure concerning attribute subsets, and the corresponding detail design is adapted to the partial information acquired by the procedure. A specially designed genetic algorithm ...