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2Embedded feature selection for neural networks via learnable drop layerLogic Journal of the IGPL. forthcoming.Feature selection is a widely studied technique whose goal is to reduce the dimensionality of the problem by removing irrelevant features. It has multiple benefits, such as improved efficacy, efficiency and interpretability of almost any type of machine learning model. Feature selection techniques may be divided into three main categories, depending on the process used to remove the features known as Filter, Wrapper and Embedded. Embedded methods are usually the preferred feature selection metho…Read more
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5Using principal component analysis to improve eathquake magnitude prediction in JapanLogic Journal of the IGPL 25 (6): 949-966. 2017.Increasing attention has been paid to the prediction of earthquakes with data mining techniques during the last decade. Several works have already proposed the use of certain features serving as inputs for supervised classifiers. However, they have been successfully used without any further transformation so far. In this work, the use of principal component analysis to reduce data dimensionality and generate new datasets is proposed. In particular, this step is inserted in a successfully already…Read more
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