  The growth of data today poses a challenge in management and inference. While
feature extraction methods are capable of reducing the size of the data for
inference, they do not help in minimizing the cost of data storage. On the
other hand, feature selection helps to remove the redundant features and
therefore is helpful not only in inference but also in reducing management
costs. This work presents a novel parallel feature selection approach for
classification, namely Parallel Feature Selection using Trace criterion (PFST),
which scales up to very large datasets. Our method uses trace criterion, a
measure of class separability used in Fisher's Discriminant Analysis, to
evaluate feature usefulness. We analyzed the criterion's desirable properties
theoretically. Based on the criterion, PFST rapidly finds important features
out of a set of features for big datasets by first making a forward selection
with early removal of seemingly redundant features parallelly. After the most
important features are included in the model, we check back their contribution
for possible interaction that may improve the fit. Lastly, we make a backward
selection to check back possible redundant added by the forward steps. We
evaluate our methods via various experiments using Linear Discriminant Analysis
as the classifier on selected features. The experiments show that our method
can produce a small set of features in a fraction of the amount of time by the
other methods under comparison. In addition, the classifier trained on the
features selected by PFST not only achieves better accuracy than the ones
chosen by other approaches but can also achieve better accuracy than the
classification on all available features.
