System and methods for feature relevance visualization optimization and filtering for explainability in AI-based alert detection and processing systems

1. A system for feature relevance visualization optimization, the system comprising:

a controller configured for modifying placement of features in a relevance visualization, the controller comprising a memory device with computer-readable program code stored thereon, a communication device connected to a network, and a processing device, wherein the processing device is configured to execute the computer-readable program code to:

receive interaction data comprising one or more features positioned in the relevance visualization, wherein the one or more features are defined and measurable properties of the interaction data;

construct a logical grouping of the one or more features based on a type of each of the one or more features, wherein similar features are collocated in the relevance visualization;

construct a machine learning-based grouping of the one or more features based on relevance calculations for the one or more features;

combine the logical grouping and the machine learning-based grouping to generate a combined feature placement, wherein the one or more features are repositioned in the relevance visualization; and

output the relevance visualization having the combined feature placement.

2. The system of claim 1 , wherein the processing device is further configured to execute the computer-readable program code to iteratively refine the combined feature placement based on changes in the interaction data.

3. The system of claim 1 , wherein the processing device is further configured to execute the computer-readable program code to receive analyst input on a placement of the one or more features in the relevance visualization, wherein the combined feature placement is at least partially based on the analyst input.

4. The system of claim 1 , wherein the machine learning-based grouping is further based on at least one of historical interaction data, streaming interaction data, analyst input, and misappropriation data.

5. The system of claim 1 , wherein repositioning the one or more features in the relevance visualization further comprises regrouping the one or more features in the combined feature placement.

6. The system of claim 1 , wherein repositioning the one or more features in the relevance visualization further comprises modifying a placement of one or more pixels in an image of the relevance visualization.

7. The system of claim 6 , wherein the processing device is further configured to execute the computer-readable program code to modify a spectrum of colors of the one or more pixels in the image of the relevance visualization based on the relevance calculations, wherein the relevance visualization is a feature relevance heat map.

8. The system of claim 6 , wherein the processing device is further configured to execute the computer-readable program code to generate a highlighted region around a portion of the one or more features in the image of the relevance visualization based on the relevance calculations.

9. The system of claim 1 , wherein the relevance visualization is a two-dimensional or three-dimensional data plot.

10. The system of claim 1 , wherein the relevance calculations and the relevance visualization are initially generated by a neural network configured to receive and process the interaction data.

11. The system of claim 10 , wherein the neural network is a convolutional neural network.

12. The system of claim 1 further comprising a machine learning model configured for user type-specific pattern recognition based on the interaction data associated with a specific user type, wherein the relevance visualization is modified based on a user type-specific pattern.

13. The system of claim 12 , wherein modifying the relevance visualization based on the user type-specific pattern comprises applying a compensating filter to the relevance visualization based on the user type-specific pattern.