Abstract:Decision fusion is the most important step in ensemble machine learning schemes. One of the greatest challenges of decision fusion is the discrete nature of decisions. This challenge causes decision fusion solutions to become variations of the voting algorithm from statistical perspective. However, increasing the redundancy of decisions imposes a serious computational challenge for real‐time systems. Resorting to fewer decisions imposes uncertainty challenges. In this Letter, the authors present a methodology … Show more
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