Novel developments in computational intelligence with applications to data stream mining

复模糊逻辑是传统模糊逻辑的一种新推广。复数模糊真值是来自复数平面的量值小于或等于1的矢量。在阐明复杂模糊逻辑的性质方面进展有限，已经出现了一些基于它的机器学习方法(我们的工作在两个研究方向上都很突出)。然而，人们认为，复杂模糊逻辑实际上是一个无穷无尽的多值逻辑家族，基于它的学习算法的设计空间也相应地是巨大的。显然，在理解这一领域方面仍有大量工作要做，我们的研究显然有机会在该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，解决关键的开放问题，如：哪些类的算子形成复杂的模糊合取、析取和蕴涵？它们的属性是什么？哪些函数构成有用的复数模糊集？我们如何从语言学上解释复杂的模糊集合？复杂的模糊集和逻辑是如何在机器学习算法中最有效地实现的，它们最适合解决哪类问题？ 为了专注于我们的研究计划，我们将追求一类应用程序。基于目前的研究成果，我们相信复杂模糊逻辑可以有效地挖掘数据流。我们的ANCFIS学习架构在时间序列预测(流数据的一个实例)中非常准确，现在我们寻求推广这一结果。要实现这一目标，我们需要回答上面提出的理论问题，因为它们都与数据流挖掘直接相关。我们的学习算法(使用识别的运算符)将以复杂模糊规则的形式提取数据流的概要，然后必须将其解释为可操作。我们最初的流挖掘问题是两个传感器数据应用程序：艾伯塔省伍德布法罗地区(油砂所在地)的空气质量监测，以及基于动物安装传感器平台的牲畜疾病监测；两者都具有重要的经济意义。

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? To focus our program of research, we will pursue one class of applications. Based on current research results, we believe that complex fuzzy logic can be effective in mining data streams. Our ANCFIS learning architecture was very accurate in time-series forecasting (an instance of stream data), and we now seek to generalize this result. Accomplishing this goal will require us to answer the theoretical questions we have raised above, as they all directly relate to data stream mining. Our learning algorithms (using the identified operators) will extract synopses of the data stream in the form of complex fuzzy rules, which must then be interpreted to be actionable. Our initial stream mining problems are a pair of sensor-data applications: air-quality monitoring in Alberta's Wood Buffalo region (site of the oilsands), and livestock disease surveillance based on animal-mounted sensor platforms; both are of significant economic importance.