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.
Deploying machine learning to improve medical diagnosis is a promising area. The purpose of this study is to identify and analyze unique genetic signatures for breast cancer grades using publicly available gene expression microarray data. The classification of cancer types is based on unsupervised feature learning. Unsupervised clustering use matrix algebra based on similarity measures which made it suitable for analyzing gene expression. The main advantage of the proposed approach is the ability to use gene expression data from different grades of breast cancer to generate features that automatically identify and enhance the cancer diagnosis. In this paper, we tested different similarity measures in order to find the best way that identifies the sets of genes with a common function using expression microarray data.
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