style="text-align:justify;"> 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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机译：style =“text-align：证明;”>许多手写数字识别系统使用完整的功能组成，以提高准确性。但是，这些系统在时间和记忆中滞后。这两个问题特别关键问题，特别是对于实时应用。因此，使用具有合适的机器学习技术的特征选择（FS）用于数字识别有助于通过最小化用于训练模型的功能数量来促进时间和内存问题。本文使用MNIST DataSet检查了具有多种分类技术的各种FS方法。此外，实施和比较不同算法的模型（ i. 线性，非线性，集合和深度学习），以研究他们对数字识别的适用性。本研究的目的是识别除了减少所需时间，计算复杂性和数字识别所需存储之外，提供至少相同的准确度作为完整功能的相关功能的子集。实验结果证明，60％的完整功能可以使用完整的特征将培训时间减少到所需时间的三分之一。此外，使用所提出的子集接受训练的分类器实现了与使用完整的功能集训练的分类器相同的准确性。
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