  A fundamental task in machine learning involves visualizing high-dimensional
data sets that arise in high-impact application domains. When considering the
context of large imbalanced data, this problem becomes much more challenging.
In this paper, 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 Oversampling
Technique (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 classification
algorithms, we show that using t-SNE on the imbalanced data and SMOTE on the
training data set, neural network classifiers have promising results without
sacrificing accuracy. Hence, we can transform the studied scientific data into
a two-dimensional (2D) space, enabling the visualization of the classifier and
the resulting decision surface using a 2D plot.
