herdiantrisufriyana/divnn: An implementation of the DeepInsight Visible Neural Network

This package facilitates application of DeepInsight (DI) and Visible Neural Network (VNN) algorithms from Alok Sharma and Michael Ku Yu, respectively. The application is intended for supervised machine learning by convolutional neural network (CNN). DeepInsight converts non-image data into image-like data by dimensionality reduction algorithms. This package maps the data into a multi-dimensional array. Meanwhile, VNN determines a neural network architecture by hierarchical clustering algorithms, particularly for data-driven ontology. This package generate a CNN model based on the ontology using the DeepInsight array as the input. However, this package includes neither dimensionality reduction nor data-driven ontology inference. A comprehensive guide to orchestrate this package and other packages to develop the DI-VNN model is described in this package vignette. The inputs are instance-feature value data frame, outcome vector, feature similarity matrix, feature three-dimensional mapping matrix, and ontology source-target-similarity-relation data frame. The outputs are tidy (expression) set, training array, and Keras CNN model.

install.packages("remotes")
remotes::install_github("herdiantrisufriyana/divnn")