A lattice computing algorithm for granular reasoning based on trapezoidal fuzzy numbers. (1st January 2013) - Jamshidi, Yazdan; Nezamabadi-pour, Hossein
Granular computing and lattice computing are two popular topics in computational intelligence. Granular reasoning is a powerful paradigm for decision making with partially ordered information where the information could be even incomplete or uncertain. In order to implement this reasoning process, lattice theory provides the requirements for the operations that can be used to define a relation between granules and computing ever-changing granules. In this regards, we describe a new algorithm named LCA-GRTFN for Granular Reasoning capable of dealing with lattice of generalised trapezoidal fuzzy numbers. To assess the effectiveness of the proposed model, eighteen benchmark datasets are tested. The results are compared favourably with those from a number of state-of-the-art machine learning techniques published in the literature. Results obtained confirm the effectiveness of the proposed method.