Input Space Partitioning for Neural Network Learning

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Terminology And Concepts

Let denote the promotion rate of two attributes i and j, which is defined by:

(1) where represents the classification error obtained by training with single attribute i, and represents the classification error obtained by training with two attributes i and j. When the promotion rate of two attributes is 1, these two attributes are considered to be mutually supportive for classification. Otherwise, they are considered to be mutually interfered for classification.

To take full advantage of inter-attribute promotions, we compute the average value of all with Any pair of attributes whose classification error is less than this average value is considered to have significant promotion to each other. The smaller the corresponding classification error is, the more significant the promotion is.

In statistics, correlation measures the strength and direction of the linear relationship between two random variables. There exist many ways of calculating correlation. This paper employs the Pearson’s correlation coefficients (Sedgwick, 2012).