Evaluating the Ambiguity of class structures via Instance Neighbor Entropy with weighting

In this paper Instance Neighbor Entropy INE with weighting was proposed to estimate the Class Structure Ambiguity (CSA) of class structures. The main idea of the INE{x)k for one instance x was to compute the weighted entropy of class probability distribution of the top k nearest neighbors of that x. The weighting associated with that entropy was determined according to the inverse of the distance between the x and the other instances. One instance was seemed as ambiguous one if most of its neighbors came from the other classes. Therefore, one class structure might be ambiguous if it contained a lot of ambiguous instances. To evaluate the effectiveness of the CSA via INE, the Pearson's correlation coefficient p between the values of accuracy achieved by SVM classifiers and the values of CSA was computed and expected to be close - 1 (perfect negative correlation) as possible. For experiments, there were two types of datasets. One was according to some seed points for each class and, for each seed point, there were a fixed number instances generated randomly under normal distribution while with class ambiguity under control. The other was selected from the LIBSVM as read world datasets. Experimental results showed that the evaluation of the CSA via INE(x)k did reveal the degree of class ambiguous with datasets generated randomly because the values of the p almost as -1, and the INE(x)k with weighted entropy evaluated more precisely than that without weighted entropy when with both types of datasets.