Many systems of handwritten digit recognition built using the complete set of
features in order to enhance the accuracy. However, these systems lagged in
terms of time and memory. These two issues are very critical issues especially
for real time applications. Therefore, using Feature Selection (FS) with suitable
machine learning technique for digit recognition contributes to facilitate
solving the issues of time and memory by minimizing the number of features
used to train the model. This paper examines various FS methods with several
classification techniques using MNIST dataset. In addition, models of different
algorithms (i.e. linear, non-linear, ensemble, and deep learning) are implemented
and compared in order to study their suitability for digit recognition.
The objective of this study is to identify a subset of relevant features that
provides at least the same accuracy as the complete set of features in addition
to reducing the required time, computational complexity, and required storage
for digit recognition. The experimental results proved that 60% of the
complete set of features reduces the training time up to third of the required
time using the complete set of features. Moreover, the classifiers trained using
the proposed subset achieve the same accuracy as the classifiers trained using
the complete set of features.
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