An Accurate and Easy to Interpret Binary Classifier Based on Association Rules Using Implication Intensity and Majority Vote

In supervised learning, classifiers range from simpler, more interpretable and generally less accurate ones (e.g., CART, C4.5, J48) to complex, neural networks, SVM). this tradeoff between interpretability accuracy, we propose a new classifier based on association rules, that is say, both easy interpret leading relevant accuracy. To illustrate proposal, its performance compared other widely used methods six open access datasets.