RMEEP – Expert System Shell with Predictive Modeling and Deep LearningGood news: Now you can develop decision support expert systems using deep learning techniques very easily. RMEEP is a perfect platform for that! RMEEP (Rulebased Model Evaluation  Event Processing) is a very powerful expert system shell rule engine, supporting predictive modeling such as neural network, self organizing maps, decision tree, regression, time series, etc. It combines rule based "forwardinference" reasoning with predictive modeling, providing a powerful platform for Deep Learning. RMEEP has been developed to use SQLlike expressions which can be learnt by business users and domain experts very easily and quickly. NonIT professional users can write RMEEP expert system rules by themselves without IT professional help. RMEEP opens a new era for business users and domain experts. SQLlike Business Rules for Business usersThe popularity of SQL as the preferred database query language has been largely due to the natural intuitiveness of the language as well as the richness of logical expressions. SQL is very easy to learn. Expressions written in SQL is very easy to understand and hence has been widely used by many nonIT professional such as business analysts and users, as well as by IT professionals. RMEEP is based on SQL99 syntax supporting most logical and arithmetic language features. Predictive ModelingThe core concept of RMEEP is Rulebased Predictive Model Evaluation. RMEEP incorporates predictive models with many logical and mathematical functions of SQL99 as patterns. By incorporating advanced predictive models into rule engine, RMEEP provides a superb platform for advanced rulebased expert systems and deep learning. RMEEP supports the following predictive models;
(Example) Deep Learning Risk ModelingThe following is a RMEEP example for risk scoring, a la Deep Learning style. Note that this uses five neural network models: Model1, Model2, Model3, Model5 and Model6. In addition, it also uses a decision tree model Model4. Total 6 predictive models. Model1, 2, 3 and 4 evaluate input data. Model5 and 6 are used to integerate results of Model1 ~ 4. In the begining, variables are declared. Rule 1, 2, 3 and 4 evaluate first four models and store on variables "Model1 score", "Model2 score", "Model3 score" and "Model4 score", respectively. Rule 5, then, computes maximum, minimum and average values of these three models. Rule 6 and 7 evaluate second tier integration models "Model5" and "Model6", using four model output values, maximum, minimum and average values. Final result scores will be stored on "Final score1" and "Final score2". Then average of these final value is computed by Rule 8. Based on "Final average" and Max/Min/Avg values, risk levels are verbalized by Rule 9 and 10. Viz;
It is noted that this model incorporates neural network, decision tree and regression as a single RMEEP model. It is a decision support expert system on a model. The following is a screenshot of of this deep learning model output on MyDataSay Android Application. MyDataSay download has this model. You can download from the link. Note that RMEEP is a feature of CMSR Data Miner, you can download and try this model with CMSR Data Miner. Downloads contain more advanced sample models.
The following is a coding of this model. It works! You just need to include your predictive models here. // declare runtime options; DECLARE OPTIONS EXPLICIT,ERRORNULL,MAXFIRE(0); // define input data fields and values in appearing order; DECLARE Gender AS STRING INPUT VALUES IN GENDER OF Model1; DECLARE Race AS STRING INPUT VALUES IN RACE OF Model1; DECLARE Jobpost AS STRING INPUT VALUES IN JOBPOST OF Model1; DECLARE CLASSIFICATION1 AS STRING INPUT VALUES IN CLASSIFICATION1 OF Model1; DECLARE EDUCLEVEL AS STRING INPUT VALUES IN EDUCLEVEL OF Model1; DECLARE AGEGROUP AS STRING INPUT VALUES IN AGEGROUP OF Model1; DECLARE Salary AS INTEGER INPUT VALUES IN SALARY OF Model1; // define output fields in appearing order; DECLARE "Model1 score", "Model2 score", "Model3 score" AS REAL OUTPUT; DECLARE "Maximum score", "Minimum score", "Average score" AS REAL OUTPUT; DECLARE "" AS STRING OUTPUT INITIAL VALUE ''; // to insert extra line in output DECLARE "Model4 ratio" AS REAL OUTPUT; DECLARE " " AS STRING OUTPUT INITIAL VALUE ''; // to insert extra line in output DECLARE "Final score1", "Final score2", "Final average" AS REAL OUTPUT; DECLARE " " AS STRING OUTPUT INITIAL VALUE ''; // to insert extra line in output DECLARE "Risk level", "Risk level (MMA)" AS STRING OUTPUT; RULE 1: // compute model 1 prediction; IF TRUE THEN SET "Model1 score" AS PREDICT(Model1) USING( GENDER AS Gender, RACE AS Race, JOBPOST AS Jobpost, CLASSIFICATION1 AS CLASSIFICATION1, EDUCLEVEL AS EDUCLEVEL, AGEGROUP AS AGEGROUP, SALARY AS Salary ) END; RULE 2: // compute model 2 prediction; IF TRUE THEN SET "Model2 score" AS PREDICT(Model2) USING( GENDER AS Gender, RACE AS Race, JOBPOST AS Jobpost, CLASSIFICATION1 AS CLASSIFICATION1, EDUCLEVEL AS EDUCLEVEL, AGEGROUP AS AGEGROUP, SALARY AS Salary ) END; RULE 3: // compute model 3 prediction; IF TRUE THEN SET "Model3 score" AS PREDICT(Model3) USING( GENDER AS Gender, RACE AS Race, JOBPOST AS Jobpost, CLASSIFICATION1 AS CLASSIFICATION1, EDUCLEVEL AS EDUCLEVEL, AGEGROUP AS AGEGROUP, SALARY AS Salary ) END; RULE 4: // compute model 4 decision tree probability of 'Risky' class; IF TRUE THEN SET "Model4 ratio" AS PREDICT(Model4, 'Risky') USING( GENDER AS Gender, RACE AS Race, JOBPOST AS Jobpost, CLASSIFICATION1 AS CLASSIFICATION1, EDUCLEVEL AS EDUCLEVEL, AGEGROUP AS AGEGROUP, SALARY AS Salary ) END; // compute max/min/avg of three neural network models; RULE 5: // decision tree output is excluded as it outputs different values; IF TRUE THEN { SET "Maximum score" AS MAX("Model1 score", "Model2 score", "Model3 score"); SET "Minimum score" AS MIN("Model1 score", "Model2 score", "Model3 score"); SET "Average score" AS AVG("Model1 score", "Model2 score", "Model3 score"); } END; RULE 6: // compute final score1 using Model5; IF TRUE THEN SET "Final score1" AS PREDICT(Model5) USING( Model1Score AS "Model1 score", Model2Score AS "Model2 score", Model3Score AS "Model3 score", Model4Score AS "Model4 ratio", // for decision tree probability MaximumScore AS "Maximum score", MinimumScore AS "Minimum score", AverageScore AS "Average score" ) END; RULE 7: // compute final score2 using Model6; IF TRUE THEN SET "Final score2" AS PREDICT(Model6) USING( Model1Score AS "Model1 score", Model2Score AS "Model2 score", Model3Score AS "Model3 score", Model4Score AS "Model4 ratio", // for decision tree probability MaximumScore AS "Maximum score", MinimumScore AS "Minimum score", AverageScore AS "Average score" ) END; RULE 8: // compute the average of final scores; IF TRUE THEN SET "Final average" AS AVG("Final score1", "Final score2") END; RULE 9: // classify risk levels; CASE WHEN "Final average" >= 0.7 THEN SET "Risk level" AS 'Very high risk' WHEN "Final average" < 0.01 THEN SET "Risk level" AS 'Low risk' WHEN "Final average" < 0.1 THEN SET "Risk level" AS 'Medium risk' WHEN "Maximum score" >= 0.6 OR "Minimum score" >= 0.2 OR "Average score" >= 0.5 THEN SET "Risk level" AS 'High risk' WHEN "Maximum score" >= 0.3 OR "Minimum score" >= 0.05 OR "Average score" >= 0.2 THEN SET "Risk level" AS 'High risk' WHEN "Maximum score" >= 0.2 OR "Minimum score" >= 0.0 OR "Average score" >= 0.1 THEN SET "Risk level" AS 'Medium risk' ELSE SET "Risk level" AS 'Low risk' END; RULE 10: // alternative classification without using final average; CASE WHEN "Maximum score" >= 0.6 OR "Minimum score" >= 0.2 OR "Average score" >= 0.5 THEN SET "Risk level (MMA)" AS 'Very high risk' WHEN "Maximum score" >= 0.3 OR "Minimum score" >= 0.05 OR "Average score" >= 0.2 THEN SET "Risk level (MMA)" AS 'High risk' WHEN "Maximum score" >= 0.2 OR "Minimum score" >= 0.0 OR "Average score" >= 0.1 THEN SET "Risk level (MMA)" AS 'Medium risk' ELSE SET "Risk level (MMA)" AS 'Low risk' END; For more and software downloads, read CMSR Data Miner. 
