Fuzzy Computing in Data Science

5Fuzzy Decision Making in Predictive Analytics and Resource Scheduling

Rekha A. Kulkarni¹*, Suhas H. Patil² and Bithika Bishesh³

¹ Dept. of Computer Engineering, SCTR’s PICT, Pune, India

² Dept. of Computer Engineering, BVUCOE, Pune, India

³ Department of Management, Sharda University, Greater Noida, India

Abstract

Decision making can be defined as the process of selecting a particular solution from a set of alternatives by gathering the information and assessing the alternative solutions. It is a reasoning process based on assumptions of values, priorities, and objectives to be achieved. Effective decision making starts with identifying a query that needs a solution, getting insights into the probable solutions, and selecting the one that better fulfils objectives. While effective decision making heavily depends on the availability of relevant information, this information may have uncertainties in the real world scenario. Uncertainty may be in the form of vagueness in data because of the inherent nature of the real-world problems, which are based on degrees of truth rather than being completely true or false.

Approximate reasoning provides a way to work with imprecise or vague data while offering an acceptable solution to a real-world situation. Fuzzy logic plays a major role in this approximate reasoning as it resembles the human decision-making methodology. Fuzzy reasoning is an attractive approach to handling imprecise, unmodeled data in solving intelligent decision-making ...