Input Weighted Data Granulation Using Hybrid Correlation Measures With Application to Metal Properties

This paper introduces a new data granulation algorithm using significance weights on the input space of the data set. This data granulation algorithm aims to provide a more reliable way of grouping data together by directing the data granulation to favor the most significant variables of the process under investigation. Such a data granulation algorithm assists in the elicitation of the initial rule-base of a fuzzy or neural-fuzzy model. A hybrid correlation index, called Significance Index, is introduced to rank the process variables based on the linear correlation coefficient and the partial correlation measure. The new algorithm is used to classify the process variables and subsequently model and predict mechanical properties of heat treated steel. The property under investigation is the Tensile Strength and the case study data set consists of chemical composition and microstructure measurements coupled with Tensile Strength measurements.