A Co-Occurrence Matrix Derived Measure of Classifiability

A n-dimensional classification problem may be visualized in (n+1) dimensions using the class label as the (n + 1)th dimension. In such visualization, the class label provides a surface which is smooth in regions where classes are non-interlaced and rough in regions where classes are interlaced. The texture of the “class label surface” thus provides an intuitive measure of pattern classifiability. Motivated by this, we propose a measure of classifiability derived from the co-occurrence matrix of labeled classification data. We establish Bayes-sense optimality of the proposed measure of classifiability and present some experimental results based on a simple algorithm to compute the proposed classifiability measure.