Geometric Comparison of Clarifications and Rule Sets

We present a technique for evaluating classifications by geometric comparison of rule sets, Rules ~e represented as objects in an n-dimensional hyperspace. The similarity of classes is computed from the overlap of the geometric class descriptions, The system produces a correlation matrix that ̄ indicates the degree of similarity between each pair of classes. The technique can be applied to classifications generated by different algorithms, with different nt~mbersof classes and different attribute sets. Experimental results from a case study in a medical domain are included. Machine Learning, Classification, Rules, Geometric Comparison