Artificial intelligence system for training a classifier

1. An artificial intelligence system for training an automated classification system, said artificial intelligence system comprising:

a) a computer implemented training database comprising a plurality of training entity records, each of the records comprising:

i) an adjudicated binary class of a training entity j, the binary class having a binary value of 1 when the class is true and a binary value of 0 when the class is false; and

ii) a plurality of class indicator tokens i for the training entity j, each of the tokens being a binary indication of the presence or absence of an indicator i in a publication by the training entity j, the binary indication having a binary value of 1 when the indication is true and a binary value of 0 when the indication is false; and

b) a computer implemented modeling engine comprising:

i) an input device, an output device, a permanent memory, a microprocessor; and

ii) computer readable instructions stored on the permanent memory, the computer readable instructions operable to cause the microprocessor to carry out the steps of:

1) read in, by the input device, the plurality of training entity records from the training database;

2) calculate, by the microprocessor, for each training entity j a tempered indication of the class, the tempered indication being based on:

a) the ratio of a weighted sum of the tokens i associated with the training entity j and a tempering factor, the tempering factor being a function of an unweighted sum of the tokens i associated with the training entity j;

b) one or more indicator weights i associated with each of the tokens i in the weighted sum; and

c) one or more tempering parameters associated with either the weighted sum or the tempering factor;

3) calculate, by the microprocessor, for each training entity j a probability of the class having a binary value of 1, wherein the calculation of the probability comprises a normalized asymptotic transformation of the tempered indication for the training entity j, the normalized asymptotic transformation having an asymptotic value of 1 for large values of the tempered indication;

4) calculate, by the microprocessor, for each training entity j a residual, the residual being the difference between the binary value of the class and the probability of the class having a binary value of 1;

5) calculate, by the microprocessor, an error function of the residuals for all of the entities;

6) calculate, by the microprocessor, values for the indicator weights i and the one or more tempering parameters that minimize the error function; and

7) output, by the output device, the indicator weights i and the one or more tempering parameters in a computer readable form to said automated classification system such that said indicator weights i and one or more tempering parameters can be used by said automated classification system to read in token data for a prospective entity h and use a model based on said indicator weights i and tempering parameters for determining a probability of said prospective entity h being in said class.

2. The artificial intelligence system of claim 1 wherein the tempered indication is set to a value of 0 when all of the tokens for a training entity j have a binary value of 0.

3. The artificial intelligence system of claim 1 wherein the tempering factor is set to a value of 1 when the unweighted sum of the tokens for a training entity j has a value of 1.

4. The artificial intelligence system of claim 3 wherein:

a) the tempering parameters comprise a tempering weight; and

b) the tempering factor increases by the tempering weight when the unweighted sum of the tokens for a training entity j increases by 1.

5. The artificial intelligence system of claim 4 wherein the tempering weight has a value of 0.1 or less.

6. The artificial intelligence system of claim 1 wherein:

a) the tempering parameters comprise a ballast factor; and

b) the ballast factor is added to the weighted sum.

7. The artificial intelligence system of claim 1 wherein the asymptotic transformation is a long tail transformation relative to a logistic transformation.

8. The artificial intelligence system of claim 1 wherein the indicators comprise words, phrases or word stems.

9. The artificial intelligence system of claim 1 wherein the error function is a sum of squares of the residuals.

10. The artificial intelligence system of claim 1 wherein:

a) said prospective entity h is a claimant and an associated insurance claim;

b) said adjudicated binary class is a determination of whether or not the insurance claim is fraudulent; and

c) said publication is a social media publication by said claimant.