A Statistical Associative Classifier with Automatic Estimation of Parameters on Computer Aided Diagnosis

In this paper, we proposed a classifier based on statistical association rules that avoids the discretization step and automatically estimates the input thresholds. The algorithm automatically selects the most significant features to produce rules. These rules are simple, including the selected features, a single interval in the antecedent of the rule and a label class in the consequent, and getting at most twice the number of rules features. To evaluate our method, we compare it with traditional classifiers as C4.5 and Adaboost, in the task of classifying benign or malign masses of mammograms, usingtwo different real datasets. The proposed method achieve the best results regarding accuracy, sensitivity and sensibility.