Method for building a self-learning evidential reasoning system

Claim 1

A method for building a self-learning evidential reasoning system comprising the steps of: defining a hierarchical model structure for the self-learning evidential reasoning system, the hierarchical model structure having a plurality of processing nodes each having a set of inputs and an output, wherein outputs from lower level processing nodes are propagated as inputs to higher level processing nodes until a final output is generated from the highest level processing node in the hierarchical model structure; specifying a number of attributes for each of the plurality of processing nodes, each attribute describing a factor to be considered by that processing node; specifying a number of possible linguistic evidential values for each of the attributes; collecting a plurality of examples for each of the processing nodes in the hierarchical model structure, each of the plurality of examples having a set of inputs and a corresponding desired output; and training the processing nodes with the plurality of examples such that the model structure learns to generate a final output from a set of inputs, the model structure being trained to have minimal error between the final output and the desired output for the plurality of examples, the training comprising the steps of: providing a linguistic evidential value for each of the inputs in the plurality of examples; translating the linguistic evidential values into an evidential numeric value; determining a weighting function for each processing node for aggregating the evidential numeric values into an evidential numeric aggregate value; and optimizing the weighting functions for each of the processing nodes in the model structure until there is minimal error between the final output and the desired output for the plurality of examples.

Claim 2

A method for building a self-learning evidential reasoning system comprising the steps of: defining a hierarchical model structure for the self-learning evidential reasoning system, the hierarchical model structure having a plurality of processing nodes each having a set of inputs and an output, wherein outputs from lower level processing nodes are propagated as inputs to higher level processing nodes until a final output is generated from the highest level processing node in the hierarchical model structure; specifying a number of attributes for each of the plurality of processing nodes, each attribute describing a factor to be considered by that processing node; specifying a number of possible linguistic evidential values for each of the attributes; collecting a plurality of examples for each of the processing nodes in the hierarchical model structure, each of the plurality of examples having a set of inputs and a corresponding desired output; and training the processing nodes with the plurality of examples such that the model structure learns to generate a final output from a set of inputs, the model structure being trained to have minimal error between the final output and the desired output for the plurality of examples, the training comprising the steps of: providing a linguistic evidential value for each of the inputs in the plurality of examples; translating the linguistic evidential values into an evidential numeric value; determining a weighting function for each processing node for aggregating the evidential numeric values into an evidential numeric aggregate value; and optimizing the weighting functions for each of the processing nodes in the model structure until there is minimal error between the final output and the desired output for the plurality of examples.

Claim 4

A method for building a self-learning evidential reasoning system for performing risk and credit analysis of financial service applications, the method comprising the steps of: defining a hierarchical model structure for performing risk and credit analysis of financial service applications, the hierarchical model structure having an input layer of processing nodes and an output layer having a processing node coupled to each of the processing nodes in the input layer; collecting a plurality of examples from a plurality of previously approved financial service applications, each example having a set of inputs and a corresponding desired output; organizing the plurality of examples into a plurality of groups used to perform risk and credit analysis; applying each of the plurality of groups to separate input layer processing nodes; and training the processing nodes with the plurality of groups such that the model structure learns to generate a final output from a set of inputs, the model structure being trained to have minimal error between the final output and the desired output for the plurality of examples, the training comprising the steps of: providing a linguistic evidential value for each of the inputs in the plurality of examples; translating the linguistic evidential values into an evidential numeric value; determining a weighting function for each processing node for aggregating the evidential numeric values into an evidential numeric aggregate value; and optimizing the weighting functions for each of the processing nodes in the model structure until there is minimal error between the final output and the desired output for the plurality of examples.

Claim 5

A method for building a self-learning evidential reasoning system for performing risk and credit analysis of financial service applications, the method comprising the steps of: defining a hierarchical model structure for performing risk and credit analysis of financial service applications, the hierarchical model structure having an input layer of processing nodes and an output layer having a processing node coupled to each of the processing nodes in the input layer; collecting a plurality of examples from a plurality of previously approved financial service applications, each example having a set of inputs and a corresponding desired output; organizing the plurality of examples into a plurality of groups used to perform risk and credit analysis; applying each of the plurality of groups to separate input layer processing nodes; and training the processing nodes with the plurality of groups such that the model structure learns to generate a final output from a set of inputs, the model structure being trained to have minimal error between the final output and the desired output for the plurality of examples, the training comprising the steps of: providing a linguistic evidential value for each of the inputs in the plurality of examples; translating the linguistic evidential values into an evidential numeric value; determining a weighting function for each processing node for aggregating the evidential numeric values into an evidential numeric aggregate value; and optimizing the weighting functions for each of the processing nodes in the model structure until there is minimal error between the final output and the desired output for the plurality of examples.