Classification of a non-monetary donation based on machine learning

A computer program product for classifying a proposed non-monetary donation, comprising one or more tangible computer-readable storage devices and program instructions stored on at least one of the one or more tangible computer-readable storage devices, the program instructions executable by a processor, the program instructions comprising:

• program instructions to train a machine learning model, the machine learning model comprising input neurons arranged in an input layer, each input neuron being operable for inputting an input value (Xn) of a parameter of the donation, and at least one hidden layer comprising multiple hidden neurons, each hidden neuron being operable for calculating a hidden layer value based on at least one input value and weights (w(Xn, Ym)) associated with the respective input neurons;
• program instructions to reduce the number of input parameters based on ranking the weights (w(Xn, Ym)) related to the individual input parameters;
• program instructions to reduce the number of neurons of the hidden layer based on ranking the weights related to the individual neurons of the hidden layer;
• and
• program instructions to provide the trained model, the reduced input parameters, and reduced hidden neurons for enabling the classification using the trained model.

A computer system for classifying a proposed non-monetary donation, the computer system comprising:

• one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage devices, and program instructions stored on at least one of the one or more storage devices for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system executes the program instructions to perform a method comprising:
• training a machine learning model, the machine learning model comprising input neurons arranged in an input layer, each input neuron being operable for inputting an input value (Xn) of a parameter of the donation, and at least one hidden layer comprising multiple hidden neurons, each hidden neuron being operable for calculating a hidden layer value based on at least one input value and weights (w(Xn, Ym)) associated with the respective input neurons;
• reducing the number of input parameters based on ranking the weights (w(Xn, Ym)) related to the individual input parameters;
• reducing the number of neurons of the hidden layer based on ranking the weights related to the individual neurons of the hidden layer;
• and
• providing the trained model, the reduced input parameters and reduced hidden neurons for enabling the classification using the trained model.