Novel developments in computational intelligence with applications to software quality

该提案将支持对计算智能和机器学习理论及其在软件可靠性建模中的应用的新研究。我们的主要理论贡献将在新的领域“复杂的模糊逻辑”，最近提出的模糊逻辑的推广。复模糊逻辑是一种新的多值逻辑，其真值为复数。目前对这种逻辑的性质以及如何应用这种逻辑知之甚少。我们的研究计划将解决这两个问题，通过开发复杂的模糊逻辑的推理逻辑基础，以及将复杂的模糊逻辑应用于实际问题的归纳学习算法。我们相信，复杂的模糊逻辑将是特别有效的时间序列预测。我们对机器学习的研究将集中在分层上，这是一种减少机器学习数据集中偏斜的方法。分层算法从多数类中删除样本和/或向少数类添加示例，从而去除或减少偏度。我们将研究各种分层算法的泛化，以及它们在商业智能中常用的高性能计算环境中的实现。

This proposal will support new investigations into the theory of computational intelligence and machine learning, and their application to modeling software reliability. Our principal theoretical contributions will be in the new area of "complex fuzzy logic," a recently proposed generalization of fuzzy logic. Complex fuzzy logic is a novel multi-valued logic, whose truth values are complex numbers. Little is currently known about the properties of this logic, or how it might be applied. Our program of research will address both of these questions, by developing both the mathematical-logic foundations of complex fuzzy logic, and inductive learning algorithms that apply complex fuzzy logic to practical problems. We believe that complex fuzzy logic will be particularly effective for time series forecasting. Our research into machine learning will focus on stratification, a method for reducing skewness in machine learning datasets. Stratification algorithms delete samples from the majority class and/or add examples to the minority class, removing or reducing skewness. We will investigate generalizations of various stratification algorithms, and their implementation in the high-performance computing environments commonly used in business intelligence.