Intelligent Decision Support Systems.ppt

7/30/2019 Intelligent Decision Support Systems.ppt

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Intelligent Decision-

Making Support Systems(iDMSS)

Dr. Saeed Shiry

Am irkabir Universi ty of Technology

Compu ter Engineer ing & Inform at ion Technology Department


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Introduction

An i-DMSS extends traditional DSS by

incorporating techniques to supply intelligent

behaviors and utilizing the power of modern

computers to support and enhance decision

making.


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Intelligent System

Intelligent systems should be able to: (i) learn or understand from experience;

(ii) make sense out of ambiguous or contradictorymessages;

(iii) respond quickly and successfully to a new situation; (iv) use reasoning in solving problems and directing

conduct effectively;

(v) deal with perplexing situations;

(vi) understand and infer in ordinary, rational ways;

(vii) apply knowledge to manipulate the environment; (viii) think and reason; and

(ix) recognize the relative importance of different elementsin a situation.


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Examples of Intelligent

Algorithms

Artificial Neural Networks (ANN)

Inductive Learning

Case-based Reasoning and AnalogicalReasoning

Genetic Algorithms

Fuzzy Logic


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Neural Computing

Neural Computing is a problem solving methodology

that attempts to mimic how our brains function.

Knowledge representations based on

Massive parallel processing

Fast retrieval of large amounts of information

The ability to recognize patterns based on historical

cases

Neural Computing = Artificial Neural Networks (ANNs)


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Artificial Neural Networks

ANN can help to automate complex decision making

Neural networks learn from past experience and

improve their performance levels

Machine learning: Methods that teach machines tosolve problems, or to support problem solving, by

applying historical cases


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Example: Loan Approval

decision Making

Loan approval decision making use many variables:Customers income, employment history, credithistory, outstanding debts, and so on. Capturingthem in a software is difficult.

Fast decision making on loans is beneficial: makedecision while customer is still in the office!

A neural network was trained to recognize patternsof successful and unsuccessful loans based on pasthistory. The NN is fed with risk, the interest rate,customer data, and other variables.

A NN can quickly recommend approval or denial ofa loan. It can also detect Fraud.


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Limitations of Neural Networks

Do not perform well at tasks that are not donewell by people

Lack explanation capabilities

Limitations and expense of hardwaretechnology restrict most applications tosoftware simulations

Training times can be excessive and tedious Usually requires large amounts of training and

test data


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Neural Network Fundamentals

Components and Structure

Processing Elements

Network

Structure of the Network

Processing Information by the Network

Inputs

Outputs Weights

Summation Function


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Neural Network

Application Development

ANN Application Development Process

1. Collect Data

2. Separate into Training and Test Sets

3. Define a Network Structure4. Select a Learning Algorithm

5. Set Parameters, values, Initialize Weights

6. Transform Data to Network Inputs

7. Start Training, and Determine and Revise Weights8. Stop and Test

9. Implementation: Use the Network with New Cases


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Data Collection and Preparation

Collect data and separate into a training set

and a test set

Use training casesto adjust the weights

Use test casesfor network validation


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Neural Network Preparation

(Non-numerical Input Data (text, pictures): preparationmay involve simplification or decomposition)

Choose the learning algorithm

Determine several parameters Learning rate (high or low)

Threshold value for the form of the output

Initial weight values

Other parameters

Choose the network's structure (nodes and layers) Select initial conditions

Transform training and test data to the requiredformat


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Training the Network

Present the training dataset to the network

Adjust weightsto produce the desired outputfor each of the inputs

Several iterations of the complete training set to get

a consistent set of weights that works for all the

training data


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Testing

Test the network after training

Examine network performance: measure thenetworks classification ability

Black box testing

Do the inputs produce the appropriate outputs?

Not necessarily 100% accurate

But may be better than human decision makers

Test plan should include Routine cases

Potentially problematic situations

May have to retrain


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Neural Computing Paradigms

Decisions the builder must make

Size of training and test data

Learning algorithms

Topology: number of processing elements and theirconfigurations

Transformation (transfer) function

Learning rate for each layer

Diagnostic and validation tools

Results in the Network's Paradigm


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NN Development Tools

Braincel (Excel Add-in)

NeuralWorks

Brainmaker PathFinder

Trajan Neural Network Simulator

NeuroShell Easy SPSS Neural Connector

MatLab


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Application and properties of Neural

Networks

Pattern recognition, learning, classification,generalization and abstraction, and interpretation ofincomplete and noisy inputs

Character, speech and visual recognition

Can provide some human problem solvingcharacteristics

Can tackle new kinds of problems

Robust

Fast Flexible and easy to maintain

Powerful hybrid systems


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Neural Computing Use

Representative Business ANN Applications

Accounting

Finance

Human Resources

Management

Marketing

Operations


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Neural Network Credit Authorizer

Construction Process

Step 1: Collect data

Step 2: Separate data into training and test sets

Step 3: Transform data into network inputs

Step 4: Select, train and test network

Step 5: Deploy developed network application


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Bankruptcy Prediction

with Neural Networks

Concept Phase

Paradigm: Three-layer network, back-propagation

Training data: Small set of well-known financial ratios

Data available on bankruptcy outcomes

Supervised network

Training time not to be a problem


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Application Design

Five Input Nodes

X1: Working capital/total assets

X2: Retained earnings/total assets

X3: Earnings before interest and taxes/total assets

X4: Market value of equity/total debt

X5: Sales/total assets

Single Output Node: Final classification for each firm

Bankruptcy or Nonbankruptcy

Development Tool: NeuroShell


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Results

ANN did better predicting 22 out of the 27 actual cases

Discriminant analysis predicted only 16 correctly

Error Analysis

Five bankrupt firms misclassified by both methods

Similar for nonbankrupt firms

Neural network at least as good as conventional

Accuracyof about 80 percent is usually acceptable for neuralnetwork applications


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Stock Market Prediction System with

Modular Neural Networks

Accurate Stock Market Prediction - Complex

Problem

Several Mathematical Models - Disappointing

Results

Fujitsu and Nikko Securities: TOPIX Buying

and Selling Prediction System


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The System

Input: Several technical and economic indexes

Several modular neural networks relate pastindexes, and buy / sell timing

Prediction system Modular neural networks

Very accurate


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Home Work 4

Read and write a summary for 2 papers out of following:

Following Paper From: Clinical Decision Support Systemsintelligent Decision-making Support Systems

Data Mining and Clinical Decision Support System

Following paper from : Encyclopedia of Decision Making andDecision Support Technologies

Neural Network Time Series Forecasting Using RecencyWeighting

The Summary should be written in Persian.

Hand over it to Papers TA by next week.

Documents

Intelligent Decision Support Systems.ppt