Atanassov Intuitionistic Fuzzy Domain Adaptation to contain negative transfer learning

Transfer learning framework is designed to use previously acquired knowledge to solve a new but somewhat related task (like humans do). Non-availability of sufficient and relevant information in building a learning model is a major bottleneck in this research area. However, such models are highly susceptible to negative transfer learning (NTL) during transferral of knowledge due to the hesitancy in the decision making. Negative transfer learning may cause chaotic learning and have a profound effect on their predictive precision. In this paper, we have proposed a novel Intuitionistic Fuzzy Domain Adaptation (IFDA) algorithm, which uses Yager-generating function over Atanassov's Intuitionistic fuzzy set theory in conjunction with modified Hausdorff Intuitionistic similarity metric to build a fuzzy domain adaptation algorithm which is independent of supervised machine learning technique. It exploits the hesitancy margin in intuitionistically fuzzified features by eradicating similar looking but useless instances. Therefore, it selects optimal source instances from a previous problem in bridging the knowledge gap, in order to solve a new target problem, by containing negative transfer learning.