intelligent decision support systems.ppt
TRANSCRIPT
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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.