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BISC Decision Support System

BISC Decision Support System. Masoud Nikravesh. Outline. BISC Decision Support System System components Applications Web-Based BISC DSS Multi-Criteria Querying Model: EC-based optimization. BISC Decision Support System. Objectives:

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BISC Decision Support System

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  1. BISC Decision Support System Masoud Nikravesh INDIN'2003, Workshop on Soft Computing...

  2. Outline • BISC Decision Support System • System components • Applications • Web-Based BISC DSS • Multi-Criteria Querying Model: EC-based optimization INDIN'2003, Workshop on Soft Computing...

  3. BISC Decision Support System Objectives: Develop soft-computing-based techniques for decision analysis • Tools to assist decision-makers in assessing the consequences of decision made in an environment of imprecision, uncertainty, and partial truth and providing a systematic risk analysis; • Tools to assist decision-makers answer “What if Questions”, examine numerous alternatives very quickly and find the value of the inputs to achieve a desired level of output; • Tools to be used with human interaction and feedback to achieve a capability to learn and adapt through time; INDIN'2003, Workshop on Soft Computing...

  4. BISC DSS: Components and Structure Model and Data Visualization • Model Management • Query • Aggregation • Ranking • Fitness Evaluation Evolutionary Kernel Genetic Algorithm, Genetic Programming, and DNA • Selection • Cross Over • Mutation Experts Knowledge Input From Decision Makers Model Representation Including Linguistic Formulation Data Management • Functional Requirements • Constraints • Goals and Objectives • Linguistic Variables Requirement INDIN'2003, Workshop on Soft Computing...

  5. BISC DSS: Process of Expert System Knowledge Base User expertise is transferred and it is stored • User Interface • Dialog Function • Knowledge Base Editor Knowledge Refinement users ask for advice or provide preferences Expert Knowledge Inference Engine Data IF … THEN Rule inferences & conclusion advises the user and explains the logic Recommendation, Advice, and Explanation INDIN'2003, Workshop on Soft Computing...

  6. BISC DSS: Data & Knowledge Management Knowledge Representation, Data Visualization and Visual Interactive Decision Making Data Sources and Warehouse (databases) Knowledge Discovery and Data Mining Knowledge Generation Expert Knowledge Knowledge Bases Organization INDIN'2003, Workshop on Soft Computing...

  7. Applications Finance stock prices and characteristics, credit scoring, credit card ranking Military battlefield simulation and decision making Medicine diagnosis Marketing store and product display electronicshopping Internetprovide knowledge and advice to large number of users Educationuniversity admissions Bankingfraud detection INDIN'2003, Workshop on Soft Computing...

  8. Case : Profitable Customers A computer system that uses customer data that allow the company to recognize good and bad customer by the cost of doing business with them and the profits they return • keep the good customers • improve the bad customers or decide to drop them • identify customers who spend money • identify customers who are profitable • compare the complex mix of marketing and servicing costs to access to new customers INDIN'2003, Workshop on Soft Computing...

  9. Case: Fraud Detection An Intelligent Computer system that can learn the user’s behavior through in mining customer databases and predicting customer behaviours (normal and irregularities) to be used to uncover, reduce or prevent fraud • in credit cards • stocks • financial markets • telecommunication • insurance INDIN'2003, Workshop on Soft Computing...

  10. Web-Based BISCDecision Support System Gamil Serag-Eldin, Masoud Nikravesh BISC The Berkeley Initiative in Soft Computing Electrical Engineering and Computer Science Department INDIN'2003, Workshop on Soft Computing...

  11. Web-based DSS: objectives • Existing search system models • using crisp logic and queries • objects need to match exactly the decision criteria which results in rigid systems with imprecise and subjective process and results • Objective: develop a multi-criteria fuzzy querying model INDIN'2003, Workshop on Soft Computing...

  12. Web-based DSS : Design Conceptual level • Resembling natural human behavior - allowing approximation • objects do not need to match exactly the decision criteria Implementation level • Designed in a generic form to: • accommodate more diverse applications • to be delivered as stand-alone software to academia and businesses. INDIN'2003, Workshop on Soft Computing...

  13. Web-based DSS Components • Fuzzy Search Engine (FSE), • Application Templates (AT), • User Interface (UI), • Database (DB), • Computational Intelligence (CI). INDIN'2003, Workshop on Soft Computing...

  14. Web-based DSS: general framework UI Application Template (AT) Computational Intelligence (CI) Fuzzy Search Engine (FSE) Aggregators Membership functions Similarity measures DB INDIN'2003, Workshop on Soft Computing...

  15. User interface & Application template Fuzzy Search Engine (FSE) UI Input mapping Control unit DB • A specific HTML interface and template for each application we developed. INDIN'2003, Workshop on Soft Computing...

  16. Database (DB) U I Fuzzy Search Engine (FSE) DB Manager Query DB User Profile • This module handles all queries or user’s profile creations from the User Interface and the Fuzzy Engine respectively. INDIN'2003, Workshop on Soft Computing...

  17. Applications • Credit Scoring • Date Matching • University Admissions • Diagnosis INDIN'2003, Workshop on Soft Computing...

  18. Multi-Aggregator Fuzzy Decision Tree:EC-based optimization Souad Souafi-Bensafi, Masoud Nikravesh BISC The Berkeley Initiative in Soft Computing Electrical Engineering and Computer Science Department INDIN'2003, Workshop on Soft Computing...

  19. Multi-Criteria Querying (1) Query Database Sj1 Sj2  SjN q1 x21 xN1 x11 q2 x22 xN2 x12     x2k qk x1k xNk • Multi-Attribute Query Similarity calculation • Query Answering • Ranking based • (criteria: number top answers) • Selection based • (criteria: threshold) Scores Scoring model INDIN'2003, Workshop on Soft Computing...

  20. Multi-Criteria Querying (2) Multi-attribute query • Scoring model: Calculation of similarity between data and query: similarity measures for crisp or fuzzy data are calculated for each attribute and combined using aggregation operators to provide a global score • User preferences Represented in the scoring model by the parameters: similarity measures, aggregation operators and corresponding parameters (weights, combination strategies) Decision making process Data INDIN'2003, Workshop on Soft Computing...

  21. First-order aggregation model (1) wk qk w1 w2 … q1 q2 … x1 x2 … xk query weights • Model decription Aggregator S(x1 , x2 , …, xk ) similarities measures Aggregation Score INDIN'2003, Workshop on Soft Computing...

  22. First-order aggregation model (2) First-order aggregation model wk w1 w2 … Specific fitness function GA-based learning module Problem specification Optimal weights • User’s preferences representation limited to weights associated with attributes • Optimization process : find the optimal weights Using GA. • Model parameters learning using GA INDIN'2003, Workshop on Soft Computing...

  23. Advanced multi-aggregation model Aggregators Attributes Aggregation tree • Model parameters learning using GP Aggregators, Attributes Optimal multi-aggregation model Specific DNA encoding Problem specification GP-based learning module Specific fitness function • Parameters • aggregators, • weights and • tree structure. • Model description INDIN'2003, Workshop on Soft Computing...

  24. Fitness calculation (1) For each Aggregation Tree For each data row xi For each attribute Query Input fuzzy data ( m1(xi)| ... | mn(xi) ) ( m1(Q)| ... | mn(Q) ) Similarity calculation Score Ranking Score calculation Score ( xi ) Aggregation Tree INDIN'2003, Workshop on Soft Computing...

  25. Fitness calculation (2) Score Ranking Overlap ==> D <= 0 Separation => D > 0 good answers good answers MaxNO MinYES D = MinYES- MaxNO MinYES MaxNO bad answers bad answers • Fitness function combines : • distance D to maximize • Tree size to minimize INDIN'2003, Workshop on Soft Computing...

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