Introduction to the Soft Computing and Intelligent Data Analysis Minitrack
2016 49th Hawaii International Conference on System Sciences (HICSS)
Cognitive Computing with Perceptions firstname.lastname@example.org Soft computing refers to a collection of computational techniques in computer science, artificial intelligence, machine learning and some engineering disciplines, which attempt to study, model, and analyze very complex phenomena: those for which more conventional methods have not yielded low cost, analytic, and complete solutions. Earlier computational approaches could model and precisely analyze only relatively simple systems.
... re complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to conventional mathematical and analytical methods. That said, it should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive. To react quickly and successfully is a matter of knowledge and the task to provide relevant, updated and useful knowledge for management is the arena for developing, building and implementing intelligent support systems. Intelligent support systems should help managers and knowledge workers to a more intuitive, practical and effective use of knowledge and information in problem solving, planning and decision-making, and should help to build innovative and creative support for operations and management. When intelligent support is built on fuzzy logic and soft computing it is being more useful for practical purposes as both short-and long-term management processes have to cope with dynamic and imprecise data and knowledge: Especially in long-term and strategic planning, in foresight and scenario building the data and information which cover 10-15 year planning horizons are bound to be uncertain and imprecise which motivates the use of soft computing models, methods and technologies. The following four papers will be presented in this year's minitrack on Soft Computing and Intelligent Data Analysis. Finding consensus in group decisions might seem like a trivial task, but whether the found consensus is actually good is a different question. In "A Dynamic Fuzzy Consensus Model with Random Iterative Steps" the authors present a new dynamic model for fuzzy consensus that uses randomness in the modeling of the individual process iterations; this leads to singular (different) consensus process paths. The imprecision of the resulting different consensus results is captured by a simple process to form an overall consensus result from the distribution of the singular consensus results. The introduction of a random term into the dynamic process captures better the nature of real world consensus reaching processes. Knowing that a certain item is part of a set is the focus of classical set theory. But often enough we only know that certain items have to be present and that the set can contain at most another set of item. Such lower and upper bounds on sets are called rough sets (RS). In "Rough Set-based Dataset Reduction Method Using Swarm Algorithm and Cluster Validation Function" an RS-based dataset reduction method using SWARM optimization algorithm and a cluster validation function is proposed. In the proposed approach, the user specifies the classification quality required in advance, and the method then finds the attribute reductions and performs attribute discretization to satisfy the desired