The t-Distributed Stochastic Neighbor Embedding (t-SNE) algorithm is used to reduce the dimensions of an earthquake engineering related data set for visualization purposes . Since imbalanced data sets greatly affect the accuracy of classifiers, we employ Synthetic Minority OversamplingTechnique (SMOTE) to tackle the imbalanced nature of such data set . We present the result obtained from t-Sne and SMOTE and compare it to the basic approaches with various aspects . Considering four options and six classificational algorithms, we show that using t-NE on the imbalance data and SMote on the training data set, neural network classifiers have promising results withoutsacrificing accuracy . Hence, we can transform the studied scientific data into a 2D space

Author(s) : Parisa Hajibabaee, Farhad Pourkamali-Anaraki, Mohammad Amin Hariri-Ardebili

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Keywords : data - smote - set - sne - imbalanced -

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