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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

BISC Decision Support System

Masoud Nikravesh

INDIN'2003, Workshop on Soft Computing...

outline
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...

bisc decision support system3
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...

bisc dss components and structure
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...

bisc dss process of expert system
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...

bisc dss data knowledge management
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...

applications
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...

case profitable customers
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...

case fraud detection
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...

web based bisc decision support system

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...

web based dss objectives
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...

web based dss design
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...

web based dss components
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...

web based dss general framework
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...

user interface application template
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...

database db
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...

applications17
Applications
  • Credit Scoring
  • Date Matching
  • University Admissions
  • Diagnosis

INDIN'2003, Workshop on Soft Computing...

multi aggregator fuzzy decision tree ec based optimization

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...

multi criteria querying 1
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...

multi criteria querying 2
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...

first order aggregation model 1
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...

first order aggregation model 2
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...

advanced multi aggregation model
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...

fitness calculation 1
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...

fitness calculation 2
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...