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Neural networks in many varieties are touted as very powerful machinelearning tools because of their ability to distill large amounts of informationfrom different forms of data, extracting complex features and enabling powerfulclassification abilities. In this study, we use neural networks to extractfeatures from both images and numeric data and use these extracted features asinputs for other machine learning models, namely support vector machines (SVMs)and k-nearest neighbor classifiers (KNNs), in order to see ifneural-network-extracted features enhance the capabilities of these models. Wetested 7 different neural network architectures in this manner, 4 for imagesand 3 for numeric data, training each for varying lengths of time and thencomparing the results of the neural network independently to those of an SVMand KNN on the data, and finally comparing these results to models of SVM andKNN trained using features extracted via the neural network architecture. Thisprocess was repeated on 3 different image datasets and 2 different numericdatasets. The results show that, in many cases, the features extracted usingthe neural network significantly improve the capabilities of SVMs and KNNscompared to running these algorithms on the raw features, and in some casesalso surpass the performance of the neural network alone. This in turn suggeststhat it may be a reasonable practice to use neural networks as a means toextract features for classification by other machine learning models for somedatasets.

Examining the Use of Neural Networks for Feature Extraction: A  Comparative Analysis using Deep Learning, Support Vector Machines, and  K-Nearest Neighbor Classifiers