Intelligent Decision-Making Support Systems (iDMSS)

1. Amirkabir University of TechnologyComputer Engineering & Information Technology Department Intelligent Decision-Making Support Systems (iDMSS) Dr. Saeed Shiry

2. 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.

3. Intelligent System • Intelligent systems should be able to: • (i) learn or understand from experience; • (ii) make sense out of ambiguous or contradictory messages; • (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 elements in a situation.

4. Examples of Intelligent Algorithms • Artificial Neural Networks (ANN) • Inductive Learning • Case-based Reasoning and Analogical Reasoning • Genetic Algorithms • Fuzzy Logic

5. 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)

6. 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 to solve problems, or to support problem solving, by applying historical cases

7. Example: Loan Approval decision Making • Loan approval decision making use many variables: Customers income, employment history, credit history, outstanding debts, and so on. Capturing them in a software is difficult. • Fast decision making on loans is beneficial: make decision while customer is still in the office! • A neural network was trained to recognize patterns of successful and unsuccessful loans based on past history. The NN is fed with risk, the interest rate, customer data, and other variables. • A NN can quickly recommend approval or denial of a loan. It can also detect Fraud.

8. Limitations of Neural Networks • Do not perform well at tasks that are not done well by people • Lack explanation capabilities • Limitations and expense of hardware technology restrict most applications to software simulations • Training times can be excessive and tedious • Usually requires large amounts of training and test data

9. Neural Network Fundamentals • Components and Structure • Processing Elements • Network • Structure of the Network • Processing Information by the Network • Inputs • Outputs • Weights • Summation Function

10. Neural NetworkApplication Development • ANN Application Development Process 1. Collect Data 2. Separate into Training and Test Sets 3. Define a Network Structure 4. Select a Learning Algorithm 5. Set Parameters, values, Initialize Weights 6. Transform Data to Network Inputs 7. Start Training, and Determine and Revise Weights 8. Stop and Test 9. Implementation: Use the Network with New Cases

11. Data Collection and Preparation • Collect data and separate into a training set and a test set • Use training cases to adjust the weights • Use test cases for network validation

12. Neural Network Preparation (Non-numerical Input Data (text, pictures): preparation may 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 required format

13. Training the Network • Present the training data set to the network • Adjust weights to produce the desired output for 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

14. Testing • Test the network after training • Examine network performance: measure the network’s 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

15. Neural Computing Paradigms Decisions the builder must make • Size of training and test data • Learning algorithms • Topology: number of processing elements and their configurations • Transformation (transfer) function • Learning rate for each layer • Diagnostic and validation tools Results in the Network's Paradigm

16. NN Development Tools • Braincel (Excel Add-in) • NeuralWorks • Brainmaker • PathFinder • Trajan Neural Network Simulator • NeuroShell Easy • SPSS Neural Connector • MatLab

17. Application and properties of Neural Networks • Pattern recognition, learning, classification, generalization and abstraction, and interpretation of incomplete and noisy inputs • Character, speech and visual recognition • Can provide some human problem solving characteristics • Can tackle new kinds of problems • Robust • Fast • Flexible and easy to maintain • Powerful hybrid systems

18. Neural Computing Use Representative Business ANN Applications • Accounting • Finance • Human Resources • Management • Marketing • Operations

19. Neural Network Credit AuthorizerConstruction 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

20. 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

21. 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

22. Architecture of the Bankruptcy Prediction Neural Network X1 X2 Bankrupt 0 X3 Nonbankrupt 1 X4 X5

23. 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 • Accuracy of about 80 percent is usually acceptable for neural network applications

24. 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

25. The System • Input: Several technical and economic indexes • Several modular neural networks relate past indexes, and buy / sell timing • Prediction system • Modular neural networks • Very accurate

26. Home Work 4 Read and write a summary for 2 papers out of following: • Following Paper From: Clinical Decision Support Systems intelligent Decision-making Support Systems Data Mining and Clinical Decision Support System • Following paper from : Encyclopedia of Decision Making and Decision Support Technologies Neural Network Time Series Forecasting Using Recency Weighting The Summary should be written in Persian. Hand over it to Papers TA by next week.