DIAGNOSTIC ENGINE AND CLASSIFIER FOR DISCOVERY OF BEHAVIORAL AND OTHER CLUSTERS RELATING TO ENTITY RELATIONSHIPS TO ENHANCE DERANDOMIZED ENTITY BEHAVIOR IDENTIFICATION AND CLASSIFICATION

1 . A system for building behavior prediction classifiers for a machine learning application comprising:

a memory for storing at least instructions;

a processor device that is operative to execute program instructions;

a database of entity behavior events;

a prediction classifier model building component comprising a predictor rule for analyzing each of a plurality inputted set of behavior events from the database of entity events and outputting a prediction classifier and a classification of each of the set of events, wherein an error for the prediction classifier is defined as random over the classification;

a diagnostic engine comprising:

an input configured to receive a permutation of the error for the at least one prediction rule and the set of classified events;

a diagnostic module configured to:

derandomize the prediction classifier; and

separate and label the irregular groupings from the derandomized events to form a diagnostic database or data package, and

output the diagnostic database or data package to an optimized classifier building component;

an optimized classifier builder component comprising one or more predictor rules for classifying derandomized relationship events and outputting an optimized predictive classifier; and

a prediction engine including a classifier configured to produce automated entity behavior predictions including classifications of derandomized behaviors.

2 . The system of claim 1 wherein the diagnostic engine module is configured to derandomize the prediction classifier by at least:

applying the permutation of the error to each of the classified set of events,

calculating the smoothness of the permuted set of events, and

applying a maximizer to the smoothed events to reveal irregular groupings of events in the smoothed data; and

separate and label the irregular groupings from the smoothed events to form the diagnostic database or data package.

3 . The system of claim 2 wherein the diagnostic engine module is configured to derandomize the prediction classifier by at least: calculating and smoothing each of the events in parallel.

4 . The system of claim 3 wherein the diagnostic engine module is configured to derandomize a region of interest with the prediction classifier.

5 . The system of claim 1 wherein the permutation is associated with the error for at least one prediction rule configured to define an overdispersion of the classified set of events.

6 . The system of claim 1 wherein the system further comprises:

the database of entity behavior events comprising events analyzed to provide a business entity rating classification; and

the predictor rule comprising a predictor for a business entity rating classification that can mask malfeasant business activity that benefits from the rating.

7 . The system of claim 6 wherein the system further comprises:

the diagnostic engine being configured to separate and label the irregular groupings from the derandomized events into a risk behavior classification for the business entity rating for the diagnostic database or data package.

8 . The system of claim 7 wherein the system further comprises:

the diagnostic engine being configured to output the diagnostic database or data package including the risk classification to the optimized classifier building component;

the optimized classifier builder component comprising one or more risk predictor rules generated from the diagnostic database; and

the prediction engine including the classifier configured to produce automated entity behavior predictions including risk classifications for the derandomized behaviors.

9 . The system of claim 1 wherein the system further comprises:

the database of entity behavior events comprising events analyzed to classify behavior events; and

the predictor rule comprising a predictor for an entity classification that can mask unknown activity unexplained by the classification.

10 . The system of claim 9 wherein the system further comprises:

the diagnostic engine being configured to separate and label the irregular groupings from the derandomized events into a classification adjacent behavior for the diagnostic database or data package.

11 . The system of claim 10 wherein the system further comprises:

the diagnostic engine being configured to output the diagnostic database or data package including the adjacent classification to the optimized classifier building component;

the optimized classifier builder component comprising one or more classification adjacent predictor rules generated from the diagnostic database; and

the prediction engine including the classifier configured to produce automated entity behavior predictions including classification-adjacent classifications for the derandomized behaviors.

12 . The system of claim 1 , wherein the system comprises a network computer.

13 . A computer implemented method for a computer comprising a memory for storing at least instructions and a processor device that is operative to execute program instructions; the method comprising:

providing a database of entity behavior events;

analyzing each of a plurality inputted set of behavior events from the database of entity events with a predictor rule;

outputting a prediction classifier and a classification of each of the set of events to a diagnostic engine, wherein an error for the prediction classifier is defined as random over the classification;

derandomize the prediction classifier using the diagnostic engine;

separate and label the irregular groupings from the derandomized events to form a diagnostic database or data package.

14 . The method of claim 13 , wherein the method further comprises:

outputting the diagnostic database or data package to an optimized classifier building component; and

classifying derandomized relationship events with the optimized classifier builder component comprising one or more of the predictor rules; and

outputting an optimized predictive classifier to a prediction engine.

15 . The method of claim 13 , wherein the method further comprises:

producing automated entity behavior predictions including classifications of derandomized behaviors with the prediction engine

16 . The method of claim 13 wherein the diagnostic engine module is configured to derandomize the prediction classifier by at least:

applying a permutation of the error to each of the classified set of events,

calculating the smoothness of the permuted set of events, and

applying a maximizer to the smoothed events to reveal irregular groupings of events in the smoothed data; and

separate and label the irregular groupings from the smoothed events to form the diagnostic database or data package.

17 . The method of claim 16 wherein the diagnostic engine module is configured to derandomize the prediction classifier by at least: calculating and smoothing each of the events in parallel.

18 . The method of claim 16 wherein the permutation is associated with the error for at least one prediction rule configured to define an overdispersion of the classified set of events.

19 . The method of claim 13 wherein the method further comprises:

providing the database of entity behavior events comprising events analyzed to provide a business entity classification rating;

wherein the predictor rule comprises a predictor for a business entity rating that can mask malfeasant business activity that benefits from the classification rating.

20 . The method of claim 19 wherein the method further comprises:

separating and labelling the irregular groupings from the derandomized events into a risk behavior classification for the business entity classification rating for the diagnostic database or data package.

21 . The method of claim 20 wherein the method further comprises:

outputting the diagnostic database or data package including the risk classification to an optimized classifier building component;

the optimized classifier builder component comprising one or more risk predictor rules generated from the diagnostic database; and

the prediction engine including the classifier configured to produce automated entity behavior predictions including risk classifications for the derandomized behaviors.

22 . The method of claim 13 wherein the method further comprises:

providing the database of entity behavior events comprising events analyzed to provide an entity classification; and

wherein the predictor rule comprising a predictor for a business entity rating that can mask unknown activity unexplained by the classification.

23 . The method of claim 22 wherein the method further comprises:

the diagnostic engine being configured to separate and label the irregular groupings from the derandomized events into an adjacent classification for the business entity rating for the diagnostic database or data package.

24 . The method of claim 23 wherein the method further comprises:

outputting the diagnostic database or data package including the adjacent classification to an optimized classifier building component;

the optimized classifier builder component comprising one or more adjacent predictor rules generated from the diagnostic database; and

the prediction engine including the classifier configured to produce automated entity behavior predictions including adjacent classifications for the derandomized behaviors.

25 . A system comprising:

a memory for storing at least instructions;

a processor device that is operative to execute program instructions;

a database of entity behavior events;

a prediction classifier building component comprising a predictor rule for analyzing each of a plurality inputted set of behavior events from the database of entity events and outputting a prediction classifier and a classification of each of the set of events, wherein an error for the prediction classifier is defined as random over the classification;

a diagnostic engine comprising:

an input configured to receive a permutation of the error for the at least one prediction rule and the set of classified events;

a diagnostic module configured to:

derandomize the prediction classifier; and

separate and label the irregular groupings from the derandomized events to form a diagnostic database or data package.