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ECOLS-research-group/Non-image-data-classification-with-CNN
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Non-image to image transformation - Algorithm 1,2,3 Copyright (c) 2019, Anuraganand Sharma - All rights reserved. This Matlab code is the implementation of the following algoirthms: Algorithm 1: Equidistant Bar Graph (Run data2imgX1.m) Algorithm 2: Normalized Distance Matrix (Run data2imgX2.m) Algorithm 3: combination of Normalized Distance Matrix and Equidistant Bar Graph algorithm (Run data2imgX3.m) These algorithms have been proposed by Anuraganand Sharma in: Sharma, A., Kumar, D. Classification with 2-D convolutional neural networks for breast cancer diagnosis. Sci Rep 12, Nature, 21857 (2022). and in the preprints: version 1: A. Sharma and D. Kumar, Non-image Data Classification with Convolutional Neural Networks. version 2: A. Sharma and D. Kumar, Classification with 2-D Convolutional Neural Networks for breast cancer diagnosis. The algoirhtm reads data given in 2D form and converts them into 2D images. Currently, it works for non-time series data only. How to run: 1 - Run data2imgX1.m or data2imgX2.m or data2imgX3.m for Algorithm 1, 2 or 3 resepectively. First, you will be asked to provide the location of the data file. 2 - It asks for data files. Data files shoould have .data extension. See the format of the sample files taken from the UCI library. Look for data files inside "data" folder. 3 - The results will be stored in the same "data" folder. 4 - The algorithm for 1D conversion also runs in the same manner.
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Non-image data classification with CNN: 3 algorithms
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