Novel developments in computational intelligence with applications to software quality

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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.