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Convolutional Neural Network (CNN) uses convolutional layers to explore spatial/temporal adjacency to construct new feature representations. So, CNN is commonly used for data with strong temporal/spatial correlations, but cannot be directly applied to generic learning tasks. In this paper, we propose to enable CNN for learning from generic data to improve classification accuracy. To take the full advantage of CNN’s feature learning power, we propose to convert each instance of the original dataset into a synthetic matrix/image format. To maximize the correlation in the constructed matrix/image, we use 0/1 optimization to reorder features and ensure that the ones with strong correlations are adjacent to each other. By using a feature reordering matrix, we are able to create a synthetic image to represent each instance. Because the constructed synthetic image preserves the original feature values and correlation, CNN can be applied to learn effective features for classification. Experiments and comparisons, on 22 benchmark datasets, demonstrate clear performance gain of applying CNN to generic datasets, compared to conventional machine learning methods. Furthermore, our method consistently outperforms approaches which directly apply CNN to generic datasets in naive ways. This research allows deep learning to be broadly applied to generic datasets. 

This paper proposes to enable deep learning for generic machine learning tasks. Our goal is to allow deep learning to be applied to data which are already represented in instance feature tabular format for a better classification accuracy. Because deep learning relies on spatial/temporal correlation to learn new feature representation, our theme is to convert each instance of the original dataset into a synthetic matrix format to take the full advantage of the feature learning power of deep learning methods. To maximize the correlation of the matrix , we use 0/1 optimization to reorder features such that the ones with strong correlations are adjacent to each other. By using a two dimensional feature reordering, we are able to create a synthetic matrix, as an image, to represent each instance. Because the synthetic image preserves the original feature values and data correlation, existing deep learning algorithms, such as convolutional neural networks (CNN), can be applied to learn effective features for classification. Our experiments on 20 generic datasets, using CNN as the deep learning classifier, confirm that enabling deep learning to generic datasets has clear performance gain, compared to generic machine learning methods. In addition, the proposed method consistently outperforms simple baselines of using CNN for generic dataset. As a result, our research allows deep learning to be broadly applied to generic datasets for learning and classification