Artificial intelligence trending system

A method for estimating a rank order for records with respect to a selected characteristic of subjects of said records, said records including attributes of said subjects, and said attributes including a categorical attribute said method comprising:

• a) a step for estimating a statistic having values relating values of said categorical attribute to said characteristic;
• b) a step for, for one of said records, applying a feature vector having elements derived from said attributes in said one record, said elements including a value of said statistic corresponding to a value of said categorical attribute in said one record, to an artificial intelligence algorithm to obtain an output value;
• c) a plurality of steps for repeating step b for each remaining one of said records;
• and
• d) a step for rank ordering said records in accordance with said output values.

A method for developing a neural network whose outputs can be used to rank order records with respect to a selected characteristic of subjects of said records, said records including attributes of said subjects, said method comprising:

• a) selecting a group of said records as training records, values of said characteristic being known for subjects of said training records;
• b) selecting a current topology and learning algorithm to configure said network;
• c) applying attributes from said training records and said known characteristic values for said subjects of said training records to said configured network to train said configured network to generate current weights;
• d) selecting a group of said records as evaluation records, values of said characteristic being known for subjects of said evaluation records;
• e) applying attributes from said evaluation records to said trained configured network to generate said outputs for said evaluation records;
• f) ordering said evaluation records in rank order in accordance with said outputs for said evaluation records;
• h) evaluating said rank order of said evaluation records in accordance with predetermined criteria;
• and
• j) modifying said current topology or learning algorithm or both to configure said network;
• and
• k) repeating steps c) through j) a plurality of times to generate a plurality of neural networks;
• and
• l) selecting one of said plurality of neural networks which best meets said criteria.

A method for estimating the relative likelihood that, or extent to which, subjects will have a characteristic, said subjects having a plurality of attributes, said attributes including a categorical attribute, said method comprising the steps of:

• a) estimating a statistic relating values of said categorical attribute to said characteristic;
• b) for each of said subjects, processing said attributes to generate an input vector descriptive of said each subject, said processing including substituting a value of said statistic for corresponding values of said categorical attribute;
• c) for each of said subjects, generating an output value as a function of said input vector;
• and
• d) using said output values as a measure of said relative likelihood or extent.

A system for estimating a rank order for records with respect to a selected characteristic of subjects of said records, said records including attributes of said subjects, and said attributes including a categorical attribute, said system comprising:

• a) means for estimating a statistic relating values of said categorical attribute to said characteristic;
• b) means for, successively applying, for each of said records, a feature vector having elements derived from said attributes in said each record, said elements including a value of said statistic corresponding to a value of said categorical attribute in said each record, to an artificial intelligence algorithm to obtain output values;
• and
• c) rank ordering said records in accordance with said output values.

A computer-readable medium carrying one or more sequences of one or more instructions for controlling a data processing system to estimate a rank order for records with respect to a selected characteristic of subjects of said records, said records including attributes of said subjects, and said attributes including a categorical attribute, the one or more sequences of one or more instructions including instructions which, when executed by one or more processors, cause the one or more processors to control said system to perform the steps of:

• a) estimating a statistic relating values of said categorical attribute to said characteristic;
• b) for one of said records, applying a feature vector having elements derived from said attributes in said one record, said elements including a value of said statistic corresponding to a value of said categorical attribute in said one record, to a artificial intelligence algorithm to obtain an output value;
• c) repeating step b for each remaining one of said records;
• and
• d) rank ordering said records in accordance with said output values.