Using Neural Networks in Decision Support Systems

Transcript Using Neural Networks in Decision Support Systems

Introduction Core Technology Building and Deploying Neural Networks

April 2005

Using Neural Networks in Decision Support Systems Medical Procedure Certification Crime Forecasting Grain Quality Assessment Jack Copper NeuralWare [email protected]

Hiroshi Maruyama SET Software Co. Ltd.

[email protected]

NeuralWare

Since 1987, NeuralWare has created and marketed neural network based Artificial Intelligence (AI) software for –

   Data Mining (clustering) Classification Forecasting

Introduction

Characteristics of Neural Network Decision Support Systems

  Integrate Data and Analytics Adapt to Changing Conditions

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Introduction

Benefits of Neural Network Decision Support Systems

   Consistent Decisions Rapid Decisions Reproducible Decisions

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Introduction

Examples of Neural Network Decision Support Systems

   Medical Procedure Certification Crime Forecasting Grain Quality Assessments

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Core Technology - Neural Networks

Output Layer

Target Target Decisions Based on Model Output Model

Hidden Layer Input Layer

Historic Data New Data

Artificial Neural Networks are connected hierarchies of Artificial Neurons (also called Processing Elements)

Building Neural Networks

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Evaluating Neural Network Performance

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Evaluating Neural Network Performance

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Deploying Neural Networks

Application Server Architecture

Server Contains Development and Run-Time Engine

Browser-based wired or wireless remote PC clients do not employ NeuralWare technology NeuralWare Technology (Run-Time Engine/Models/FlashCode) embedded in Server © 2005 NeuralWare. All rights reserved.

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Deploying Neural Networks

Distributed Intelligence Architecture

Server Contains Development Engine

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Case Study – Medical Procedure Certification Objectives

  Reduce Workload on Doctors and Registered Nurses Improve Responsiveness to Customers (faster decisions)

Challenges

  No “Gold Standard” for decisions – even Doctors sometimes disagree Inconsistent data formats and labeling

Process

  Used NeuralSight to build and evaluate ~ 30,000 Models in 3 weeks Developed prototype software to permit altering Model decision threshold

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Case Study – Medical Procedure Certification

Performance of best models (ranked by Average Classification Rate) for the Global model and CT and MRI Modality models

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Case Study – Medical Procedure Certification

Metric Database Acquire/Validate Case Input Retrieve Metrics Select/Execute Model Apply Thresholds Update Metrics Process Manually NO YES Approve Procedure?

YES Selected for Audit?

DONE

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Case Study – Crime Forecasting

Objectives

  Identify Patterns in Criminal Activity that indicate Potential Future Trouble Spots Redirect Police Resources to Focus on Areas where Serious Crime is expected to Increase

Challenges

  Defining Crime Categories and Severity Levels Inconsistent data formats and labeling; missing or non-existent data

Process

  Used NeuralSight to Build and Evaluate ~ 10,000 Models in 1 week On-going evaluation by researchers at Carnegie Mellon University

Case Study – Crime Forecasting

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Case Study – Crime Forecasting

How to Forecast Change in Crime Police know current crime levels

 Have allocated resources to respond to existing crimes

Most valuable information for tactical level planning:

  Where is crime likely to have large increases next month?

Forecast crime by area and calculate: Forecasted Change (t+1) = Forecast (t+1) – Actual (t) The Benefit – Better Allocation of Scarce Resources

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Case Study – Crime Forecasting

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Case Study – Grain Quality Assessment

Objectives

  Provide a Platform for rapidly and consistently assessing the quality of grain Maintain detailed records of tests and build foundation for data mining

Challenges

  No “Gold Standard” for decisions – even experienced human inspectors are inconsistent Requires tedious work to identify wide variety of training data samples

Process

  Used Predict and NeuralSight to Build and Evaluate many thousands of Models Now developing image database to support agriculture research

Case Study – Grain Quality Assessment

An Instrument – and examples of seed images

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Case Study – Grain Quality Assessment

Many (more than 300) initial features per seed

 Predict Variable Selection found a much smaller set of features to use in building models

The characteristics of grain that are important are difficult even for human inspectors to identify

 Multiple neural networks are used to make the hard decisions

The value of wheat and other commodities depends on its quality – millions of dollars are at risk if quality decisions are incorrect!

What Have you Learned?

Neural Networks make Powerful Decision Support Systems

 Human Judgment Determines the Cost/Benefit Tradeoff for Accuracy

Know your Problem !

Neural Network Decisions are based on Learning Patterns

 Relationships in Historical Data are the basis for Current Action

Know your Data !

Thank You !

Jack Copper NeuralWare [email protected]

Hiroshi Maruyama SET Software Co. Ltd.

[email protected]

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Using Neural Networks in Decision Support Systems

Transcript Using Neural Networks in Decision Support Systems

NeuralWare

Introduction

Introduction

Introduction

Building Neural Networks

Evaluating Neural Network Performance

Evaluating Neural Network Performance

Deploying Neural Networks

Deploying Neural Networks

Case Study – Crime Forecasting

Case Study – Crime Forecasting

Case Study – Crime Forecasting

Case Study – Crime Forecasting

Case Study – Grain Quality Assessment

Case Study – Grain Quality Assessment

Case Study – Grain Quality Assessment

What Have you Learned?

Thank You !

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