Fuzzy classification is a widely explored research solution of objects in data sciences and engineering. With the span of time, it got new heights with significant improvements according to the needs. Still there are some issues to be discussed and solved in a fuzzy manner; fuzzy classification of imbalanced data is one of them. Consequently, the importance of fuzzy nearest neighbor came into the scenario and deployed in many applications. Various improved crisp nearest neighbor approaches are performing well on imbalanced data-sets, but not much work has done on the fuzzy nearest neighbor for imbalanced data. In this paper, we propose to find out correct memberships of test instances from imbalanced data by merging an adaptive K-nearest neighbor approach to deal with the imbalanced issue and then join it with fuzzy K-nearest neighbor. © 2019, © 2019 IETE.