Скачать презентацию CHAPTER 16 Neural Computing Applications and Advanced Artificial Скачать презентацию CHAPTER 16 Neural Computing Applications and Advanced Artificial

3ed47270e02f77d13d98a4d6ebea51a3.ppt

  • Количество слайдов: 62

CHAPTER 16 Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications 1 CHAPTER 16 Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications 1

Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications n n Several Real-World Neural Computing Applications, and Advanced Artificial Intelligent Systems and Applications n n Several Real-World Applications of ANN Technology Advanced AI Systems – Genetic Algorithms – Fuzzy Logic – Qualitative Reasoning n Integration (Hybrids) 2 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Areas of ANN Applications: An Overview Representative Business ANN Applications n n n Accounting Areas of ANN Applications: An Overview Representative Business ANN Applications n n n Accounting Finance Human Resources Management Marketing Operations 3 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Accounting n n Identify tax fraud Enhance auditing by finding irregularities 4 Decision Support Accounting n n Identify tax fraud Enhance auditing by finding irregularities 4 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Finance n n n n Signatures and bank note verifications Mortgage underwriting Foreign exchange Finance n n n n Signatures and bank note verifications Mortgage underwriting Foreign exchange rate forecasting Country risk rating Bankruptcy prediction Customer credit scoring Credit card approval and fraud detection Stock and commodity selection and trading 5 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Finance 2 n n n n Credit card profitability Forecasting economic turning points Bond Finance 2 n n n n Credit card profitability Forecasting economic turning points Bond rating and trading Pricing initial public offerings Loan approvals Economic and financial forecasting Risk management 6 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Human Resources n n Predicting employees’ performance and behavior Determining personnel resource requirements 7 Human Resources n n Predicting employees’ performance and behavior Determining personnel resource requirements 7 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Management n n Corporate merger prediction Country risk rating 8 Decision Support Systems and Management n n Corporate merger prediction Country risk rating 8 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Marketing n n n n Consumer spending pattern classification Customers’ characteristics Sales forecasts Data Marketing n n n n Consumer spending pattern classification Customers’ characteristics Sales forecasts Data mining Airline fare management Direct mail optimization Targeted marketing 9 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Operations n n n n Airline crew scheduling Predicting airline seat demand Vehicle routing Operations n n n n Airline crew scheduling Predicting airline seat demand Vehicle routing Assembly and packaged goods inspection Quality control Matching jobs to candidates Production/job scheduling Factory process control Many More 10 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Credit Approval with Neural Networks n Increases loan processor productivity by 25 to 35% Credit Approval with Neural Networks n Increases loan processor productivity by 25 to 35% over other computerized tools n Also detects credit card fraud 11 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

The ANN Method n Data from the application and into a database n Preprocess The ANN Method n Data from the application and into a database n Preprocess applications manually n Neural network trained in advance with many good and bad risk cases 12 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Neural Network Credit Authorizer Construction Process n Step 1: Collect data n Step 2: Neural Network Credit Authorizer Construction Process n Step 1: Collect data n Step 2: Separate data into training and test sets n Step 3: Transform data into network inputs n Step 4: Select, train, and test network n Step 5: Deploy developed network application 13 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Bankruptcy Prediction with Neural Networks Concept Phase n Paradigm: Three-layer network, back-propagation n Training Bankruptcy Prediction with Neural Networks Concept Phase n Paradigm: Three-layer network, back-propagation n Training data: Small set of well-known financial ratios n Data available on bankruptcy outcomes n Supervised network n Training time not to be a problem 14 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Application Design n Five Input Nodes X 1: Working capital/total assets X 2: Retained Application Design n Five Input Nodes X 1: Working capital/total assets X 2: Retained earnings/total assets X 3: Earnings before interest and taxes/total assets X 4: Market value of equity/total debt X 5: Sales/total assets n Single Output Node: Final classification for each firm – Bankruptcy or – Nonbankruptcy n Development Tool: Neuro. Shell Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 15

n Development – Three-layer network with backpropagation (Figure 16. 3) – Continuous valued input n Development – Three-layer network with backpropagation (Figure 16. 3) – Continuous valued input – Single output node: 0 = bankrupt, 1 = not bankrupt n Training – Data Set: 129 firms – Training Set: 74 firms; 38 bankrupt, 36 not – Ratios computed and stored in input files for: • The neural network • A conventional discriminant analysis program 16 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Architecture of the Bankruptcy Prediction Neural Network (Figure 16. 3) X 1 X 2 Architecture of the Bankruptcy Prediction Neural Network (Figure 16. 3) X 1 X 2 Bankrupt 0 X 3 X 4 Not bankrupt 1 X 5 17 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n Parameters – Learning threshold – Learning rate – Momentum n Testing – Two n Parameters – Learning threshold – Learning rate – Momentum n Testing – Two Ways • Test data set: 27 bankrupt firms, 28 nonbankrupt firms • Comparison with discriminant analysis – The neural network correctly predicted: • 81. 5 percent bankrupt cases • 82. 1 percent nonbankrupt cases 18 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n ANN did better predicting 22 out of the 27 actual cases n Discriminant n ANN did better predicting 22 out of the 27 actual cases n Discriminant analysis predicted only 16 correctly n Error Analysis – Five bankrupt firms misclassified by both methods – Similar for nonbankrupt firms n Neural network at least as good as conventional n Accuracy of about 80 percent is usually acceptable for neural network applications 19 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Stock Market Prediction System with Modular Neural Networks n Accurate Stock Market Prediction - Stock Market Prediction System with Modular Neural Networks n Accurate Stock Market Prediction - Complex Problem n Several Mathematical Models - Disappointing Results n Fujitsu and Nikko Securities: TOPIX Buying and Selling Prediction System 20 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n Input: Several technical and economic indexes n Several modular neural networks relate past n Input: Several technical and economic indexes n Several modular neural networks relate past indexes, and buy/sell timing n Prediction system – Modular neural networks – Very accurate 21 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Network Architecture (Figure 16. 4) n Network Model: 3 layers, standard sigmoid function, continuous Network Architecture (Figure 16. 4) n Network Model: 3 layers, standard sigmoid function, continuous output [0, 1] n High-speed Supplementary Learning Algorithm n Training Data – Data Selection – Training Data 22 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n Preprocessing: Input Indexes - Converted into spatial patterns, preprocessed to regularize them n n Preprocessing: Input Indexes - Converted into spatial patterns, preprocessed to regularize them n Moving Simulation Prediction Method (Figure 16. 5) n Result of Simulations – Simulation for Buying and Selling Stocks – Example (Figure 16. 6) – Excellent Profit 23 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Integrated ANNs and Expert Systems 1. Resource Requirements Advisor – Advises users on database Integrated ANNs and Expert Systems 1. Resource Requirements Advisor – Advises users on database systems’ resource requirements – Predicts the time and effort to finish a database project – ES shell AUBREY and neural network tool Neuro. Shell – ES supported data collection – ANN used for data evaluation – ES final analysis 24 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

2. Personnel Resource Requirements Advisor – Project personnel resource requirements for maintaining networks or 2. Personnel Resource Requirements Advisor – Project personnel resource requirements for maintaining networks or workstations at NASA – Rule-based ES determines the final resource projections – ANN provides project completion times for services requested (Figure 16. 7) 25 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

3. Diagnostic System for an Airline – Singapore Airlines – Assists technicians in diagnosing 3. Diagnostic System for an Airline – Singapore Airlines – Assists technicians in diagnosing avionics equipment – INSIDE (Inertial Navigation System Interactive Diagnostic Expert) – Designed to reduce the diagnostic time (Figure 16. 8) 26 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

4. Manufacturing Product Liability – – United Technologies Carrier Two ES + ANN Patterns 4. Manufacturing Product Liability – – United Technologies Carrier Two ES + ANN Patterns fed into multilayer feedforward ANN Integrated with a database into an Automatic Early Warning System (AEWS) 27 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

5. Oil Refinery Production Scheduling and Environmental Control – – – Citgo Petroleum Corporation 5. Oil Refinery Production Scheduling and Environmental Control – – – Citgo Petroleum Corporation Lower costs Improved safety Higher product quality Higher yields 28 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Genetic Algorithms n n n Goal (evolutionary algorithms): Demonstrate selforganization and adaptation by exposure Genetic Algorithms n n n Goal (evolutionary algorithms): Demonstrate selforganization and adaptation by exposure to the environment System learns to adapt to changes. Example 1: Vector Game – Random trial and error – Genetic algorithm solution n n Process (Figure 16. 9) Example: the game of Master. Mind 29 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Genetic Algorithm Definition and Process Genetic algorithm: Genetic Algorithm Definition and Process Genetic algorithm: "an iterative procedure maintaining a population of structures that are candidate solutions to specific domain challenges” (Grefenstette [1982]) n Each candidate solution is called a chromosome n Chromosomes can copy themselves, mate, and mutate n Use specific genetic operators - reproduction, crossover and mutation 30 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Primary Operators of Most Genetic Algorithms n Reproduction n Crossover n Mutation 31 Decision Primary Operators of Most Genetic Algorithms n Reproduction n Crossover n Mutation 31 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Genetic Algorithm Operators Parent 1 1 0 1 1 1 Parent 2 1 1 Genetic Algorithm Operators Parent 1 1 0 1 1 1 Parent 2 1 1 0 0 0 1 1 Child 1 1 0 0 1 1 Child 2 1 1 0 0 1 1 0 Mutation 32 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

GA Example: The Knapsack Problem n n n n n Item: 1 2 3 GA Example: The Knapsack Problem n n n n n Item: 1 2 3 4 5 6 7 Benefit: 5 8 3 2 7 9 4 Weight: 7 8 4 10 4 6 4 Knapsack holds a maximum of 22 pounds Fill it to get the maximum benefit Solutions take the form of a string of 1’s Solution: 1 1 0 0 Means choose items 1, 2, 5. Weight = 21, Benefit = 20 Evolver solution in Figure 16. 10 33 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Genetic Algorithms Applications and Software n Type of machine learning n Set of efficient, Genetic Algorithms Applications and Software n Type of machine learning n Set of efficient, domain-independent search heuristics for a broad spectrum of applications 34 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Genetic Algorithm Application Areas n n n Dynamic process control Induction of rule optimization Genetic Algorithm Application Areas n n n Dynamic process control Induction of rule optimization Discovering new connectivity topologies Simulating biological models of behavior and evolution Complex design of engineering structures Pattern recognition Scheduling Transportation Layout and circuit design Telecommunication Graph-based problems 35 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Business Applications n Channel 4 Television (England) to schedule commercials Driver scheduling in a Business Applications n Channel 4 Television (England) to schedule commercials Driver scheduling in a public transportation system Jobshop scheduling Assignment of destinations to sources Trading stocks Productivity in whisky-making is increased n Often genetic algorithm hybrids with other AI methods n n n 36 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Representative Commercial Packages n n n Evolver (Excel spreadsheet add-in) Genetic Algorithm User Interface Representative Commercial Packages n n n Evolver (Excel spreadsheet add-in) Genetic Algorithm User Interface (GAUI) OOGA (Object-Oriented GA for industrial use) Xper. Rule Genasys (ES shell with an embedded genetic algorithm) Sugal Genetic Algorithm Simulator 37 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Optimization Algorithms n Via neural computing sometimes n Genetic algorithms and their derivatives can Optimization Algorithms n Via neural computing sometimes n Genetic algorithms and their derivatives can optimize (or nearly optimize) complex problems 38 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Fuzzy Logic n Fuzzy logic deals with uncertainty n Uses the mathematical theory of Fuzzy Logic n Fuzzy logic deals with uncertainty n Uses the mathematical theory of fuzzy sets n Simulates the process of normal human reasoning n Allows the computer to behave less precisely and logically Decision making involves gray areas and the term maybe 39 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Fuzzy Logic Advantages n n n n n Provides flexibility Provides options Frees the Fuzzy Logic Advantages n n n n n Provides flexibility Provides options Frees the imagination More forgiving Allows for observation Shortens system development time Increases the system's maintainability Uses less expensive hardware Handles control or decision-making problems not easily defined by mathematical models 40 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Fuzzy Logic Example: What is Tall? In-Class Exercise Proportion Height Voted for 5’ 10” Fuzzy Logic Example: What is Tall? In-Class Exercise Proportion Height Voted for 5’ 10” 0. 05 5’ 11” 0. 10 6’ 0. 60 6’ 1” 0. 15 6’ 2” 0. 10 n – Jack is 6 feet tall – Probability theory - cumulative probability – There is a 75 percent chance that Jack is tall 41 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n n Fuzzy logic - Jack's degree of membership within the set of tall n n Fuzzy logic - Jack's degree of membership within the set of tall people is 0. 75 We are not completely sure whether he is tall or not Fuzzy logic - We agree that Jack is more or less tall Membership Function < Jack, 0. 75 Tall > n Knowledge-based system approach: Jack is tall =. 75) Belief functions n Can use fuzzy logic in rule-based systems n (CF 42 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Membership Functions in Fuzzy Sets (Figure 16. 11) Short Medium Tall 1. 0 Membership Membership Functions in Fuzzy Sets (Figure 16. 11) Short Medium Tall 1. 0 Membership 0. 5 64 74 69 Height in inches (1 inch = 2. 54 cm) 43 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Fuzzy Logic Applications and Software n Difficult to apply when people provide evidence n Fuzzy Logic Applications and Software n Difficult to apply when people provide evidence n Used in consumer products that have sensors – – – Air conditioners Cameras Dishwashers Microwaves Toasters n Special software packages n Controls applications Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ 44

Examples of Fuzzy Logic Example 1: Strategic planning – STRATASSIST - fuzzy expert system Examples of Fuzzy Logic Example 1: Strategic planning – STRATASSIST - fuzzy expert system that helps small- to medium-sized firms plan strategically for a single product Example 2: Fuzziness in real estate Example 3: A fuzzy bond evaluation system 45 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Fuzzy Logic Software n n Fuzzy Inference Development Environment (FIDE) Z Search Hyper. Logic Fuzzy Logic Software n n Fuzzy Inference Development Environment (FIDE) Z Search Hyper. Logic Corporation demos Others 46 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Qualitative Reasoning (QR) – Means of representing and making inferences using general, physical knowledge Qualitative Reasoning (QR) – Means of representing and making inferences using general, physical knowledge about the world – QR is a model-based procedure that consequently incorporates deep knowledge about a problem domain – Typical QR Logic • “If you touch a kettle full of boiling water on a stove, you will burn yourself” • “If you throw an object off a building, it will go down” 47 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n But n No specific knowledge about boiling temperature, just that it is really n But n No specific knowledge about boiling temperature, just that it is really hot! n No specific information about the building or object, unless you are the object, or you are trying to catch it 48 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n Main goal of QR: To represent common sense knowledge about the physical world, n Main goal of QR: To represent common sense knowledge about the physical world, and the underlying abstractions used in quantitative models (objects fall) n Given such knowledge and appropriate reasoning methods, an ES could make predictions and diagnoses, and explain the behavior of physical systems qualitatively, even when exact quantitative descriptions are unavailable or intractable 49 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Qualitative Reasoning n Relevant behavior is modeled n Temporal and spatial qualities in decision Qualitative Reasoning n Relevant behavior is modeled n Temporal and spatial qualities in decision making are represented effectively n Applies common sense mathematical rules to variables and functions n There are structure rules and behavior rules 50 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Some Real-World QR Applications n Nuclear plant fault diagnoses n Business processes n Financial Some Real-World QR Applications n Nuclear plant fault diagnoses n Business processes n Financial markets n Economic systems 51 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Intelligent Systems Integration n Combine – – n n Neural Computing Expert Systems Genetic Intelligent Systems Integration n Combine – – n n Neural Computing Expert Systems Genetic Algorithms Fuzzy Logic Example: International investment management-stock selection Fuzzy Logic and ANN (Fuzzy. Net) to forecast the expected returns from stocks, cash, bonds, and other assets to determine the optimal allocation of assets 52 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n n Global markets Integrated network architecture of the system (Figure 16. 12) Technologies n n Global markets Integrated network architecture of the system (Figure 16. 12) Technologies n n n Expert system (rule-based) for country and stock selection Neural network forecasting Fuzzy logic for assessing factors without reliable data 53 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Fuzzy. Net Architecture (Figure 16. 12) n Membership Function Generator (MFG) n Fuzzy Information Fuzzy. Net Architecture (Figure 16. 12) n Membership Function Generator (MFG) n Fuzzy Information Processor (FIP) n Back-propagation Neural Network (BPN) 54 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Data Mining and Knowledge Discovery in Databases (KDD) n n Hidden value in data Data Mining and Knowledge Discovery in Databases (KDD) n n Hidden value in data Knowledge Discovery in Databases (KDD) 55 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

The KDD Process Start with Raw Data and Do 1. Selection to produce target The KDD Process Start with Raw Data and Do 1. Selection to produce target the appropriate data which undergoes 2. Preprocessing to filter the data in preparation for 3. Transformation so that 4. Data Mining can identify patterns that go through 5. Interpretation and Evaluation resulting in knowledge 56 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Data Mining n Find kernels of value in raw data ore n Theoretical advances Data Mining n Find kernels of value in raw data ore n Theoretical advances – Knowledge discovery in textual databases – Methods based on statistics, cluster analysis, discriminant analysis, fuzzy logic, genetic algorithms, and neural networks – Ideal for data mining 57 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

AI Methods and Data Mining for Search n Neural Networks n Expert Systems n AI Methods and Data Mining for Search n Neural Networks n Expert Systems n Rule Induction 58 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Data Mining Applications Areas n Marketing n Investment n Fraud detection n Manufacturing 59 Data Mining Applications Areas n Marketing n Investment n Fraud detection n Manufacturing 59 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Information Overload n Data mining methods can sift through soft information to identify relationships Information Overload n Data mining methods can sift through soft information to identify relationships automatically n Intelligent agents 60 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

Important KDD and Data Mining Challenges n Dealing with larger databases n Working with Important KDD and Data Mining Challenges n Dealing with larger databases n Working with higher dimensionalities of data n Overfitting--modeling noise rather than data patterns n Assessing statistical significance of results n Working with constantly changing data and knowledge Continue 61 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ

n Working through missing and noisy data n Determining complex relationships between fields n n Working through missing and noisy data n Determining complex relationships between fields n Making patterns more understandable to humans n Providing better user interaction and prior knowledge about the data n Providing integration with other systems 62 Decision Support Systems and Intelligent Systems, Efraim Turban and Jay E. Aronson 6 th ed, Copyright 2001, Prentice Hall, Upper Saddle River, NJ