Method and system for improving classifications performed by an artificial neural network (ANN) model

A system for improving classifications performed by an Artificial Neural Network (ANN) model, the system comprising:

• an ANN classification device comprising at least one processor and a computer-readable medium storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:
• identifying, for a classification performed by the ANN model for an input, one or more activated neurons in one or more neural layers of the ANN model, wherein the one or more activated neurons are neurons achieving respective activation threshold values for the input and corresponding to one or more relevant features associated with the input;
• analyzing the one or more activated neurons in the one or more neural layers with respect to one or more Characteristic Feature Directive (CFDs) for the one or more neural layers and for a correct class of the input, wherein the one or more CFDs are generated after a training phase of the ANN model and based on one or more neurons in the one or more neural layers that are activated for a training input of the correct class;
• determining one or more differentiating neurons in the one or more neural layers based on the analysis, wherein the one or more differentiating neurons are neurons that are not activated as per the one or more CFDs for the correct class of the input;
• and
• providing one or more missing features based on the one or more differentiating neurons in the one or more neural layers for improving classifications performed by the ANN model.

A non-transitory computer-readable medium storing computer-executable instructions for improving classifications performed by an Artificial Neural Network (ANN) model, the computer-executable instructions configured for:

• identifying, for a classification performed by the ANN model for an input, one or more activated neurons in one or more neural layers of the ANN model, wherein the one or more activated neurons are neurons achieving respective activation threshold values for the input and corresponding to one or more relevant features associated with the input;
• analyzing the one or more activated neurons in the one or more neural layers with respect to one or more Characteristic Feature Directive (CFDs) for the one or more neural layers and for a correct class of the input, wherein the one or more CFDs are generated after a training phase of the ANN model and based on one or more neurons in the one or more neural layers that are activated for a training input of the correct class;
• determining one or more differentiating neurons in the one or more neural layers based on the analysis, wherein the one or more differentiating neurons are neurons that are not activated as per the one or more CFDs for the correct class of the input;
• and
• providing one or more missing features based on the one or more differentiating neurons in the one or more neural layers for improving classifications performed by the ANN model.