Towards an improved classification model based on Deep Learning and nearest rules strategy

Mohammed El Fouki, Noura Aknin, Kamal Eddine El Kadiri


In this paper we present a comparison between two improved approaches i.e. hybrid rules-based method with a proposed wrapper and nearest rule strategy, deep principal component analysis. We also perform several experiments with an analysis dataset from a distance learning platform. Several classifiers were developed to compare the performance of the proposed approaches,  using  accuracy, TP rate, F measure, PRC area, MCC, precision, recall  and receiver  operating  characteristics area  (AROC)  as  metrics. The result confirms the utility of these algorithms for classification and shows clearly the superiority of our approaches

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