Novel developments in computational intelligence with applications to data stream mining

计算智能的新发展及其在数据流挖掘中的应用

• 批准号: 262151-2012
• 负责人: Dick, Scott
• 金额: $ 1.6万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2014
• 资助国家: 加拿大
• 起止时间: 2014-01-01 至 2015-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=548001
• 关键词: Novel; developments; computational; intelligence; applications

Complex fuzzy logic is a recent generalization of the traditional fuzzy logic. Complex fuzzy truth values are vectors from the complex plane with a magnitude less than or equal to 1. There has been limited progress in elucidating the properties of complex fuzzy logic, and a few machine-learning approaches based on it have appeared (with our work prominent in both strands of research). However, it is believed that complex fuzzy logic is actually an infinite family of multivalued logics; and that the design space for learning algorithms based on it is correspondingly vast. Plainly, there is still an enormous amount of work to be done in understanding this area, and a clear opportunity for our research to make a lasting mark on the field. Our proposed program of research for the coming five years will continue our theoretical and applied research in this field, tackling key open questions such as: what are the classes of operators that form complex fuzzy conjunctions, disjunctions, and implications? What are their properties? What functions form useful complex fuzzy sets? How do we linguistically interpret complex fuzzy sets? How are complex fuzzy sets and logic most usefully realized in machine-learning algorithms, and what classes of problems are they best-suited to solve?

复模糊逻辑是传统模糊逻辑的最新推广。复模糊真值是复平面上大小小于等于1的向量。在阐明复杂模糊逻辑的性质方面进展有限，并且已经出现了一些基于它的机器学习方法（我们的工作在这两个研究方向中都很突出）。然而，人们认为复模糊逻辑实际上是一个无限族的多值逻辑；而基于它的学习算法的设计空间也相应地很大。显然，在理解这一领域仍有大量的工作要做，我们的研究显然有机会在这一领域留下持久的印记。我们提出的未来五年的研究计划将继续我们在这一领域的理论和应用研究，解决关键的开放性问题，如：构成复杂模糊连接、析取和暗示的算子的类别是什么？它们的性质是什么？什么函数构成有用的复模糊集？我们如何在语言上解释复杂的模糊集？在机器学习算法中，复杂的模糊集和逻辑是如何最有用地实现的？它们最适合解决哪类问题？