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The growth of data today poses a challenge in management and inference. Whilefeature extraction methods are capable of reducing the size of the data forinference, they do not help in minimizing the cost of data storage. On theother hand, feature selection helps to remove the redundant features andtherefore is helpful not only in inference but also in reducing managementcosts. This work presents a novel parallel feature selection approach forclassification, namely Parallel Feature Selection using Trace criterion (PFST),which scales up to very large datasets. Our method uses trace criterion, ameasure of class separability used in Fisher's Discriminant Analysis, toevaluate feature usefulness. We analyzed the criterion's desirable propertiestheoretically. Based on the criterion, PFST rapidly finds important featuresout of a set of features for big datasets by first making a forward selectionwith early removal of seemingly redundant features parallelly. After the mostimportant features are included in the model, we check back their contributionfor possible interaction that may improve the fit. Lastly, we make a backwardselection to check back possible redundant added by the forward steps. Weevaluate our methods via various experiments using Linear Discriminant Analysisas the classifier on selected features. The experiments show that our methodcan produce a small set of features in a fraction of the amount of time by theother methods under comparison. In addition, the classifier trained on thefeatures selected by PFST not only achieves better accuracy than the oneschosen by other approaches but can also achieve better accuracy than theclassification on all available features.

Parallel feature selection based on the trace ratio criterion