Machine Learning Techniques for the Evaluating of External Skeletal Fixation Structure Dr.Khaled Rasheed Dr. Walter D. Potter Dr. Dennis N. Aron Ning Suo.

Slides:

Advertisements

Similar presentations

Ch. Eick: More on Machine Learning & Neural Networks Different Forms of Learning: –Learning agent receives feedback with respect to its actions (e.g. using.

Advertisements

SAK 5609 DATA MINING Prof. Madya Dr. Md. Nasir bin Sulaiman

1 Lecture 5: Automatic cluster detection Lecture 6: Artificial neural networks Lecture 7: Evaluation of discovered knowledge Brief introduction to lectures.

1 On Constructing Efficient Shared Decision Trees for Multiple Packet Filters Author: Bo Zhang T. S. Eugene Ng Publisher: IEEE INFOCOM 2010 Presenter:

Final Project: Project 9 Part 1: Neural Networks Part 2: Overview of Classifiers Aparna S. Varde April 28, 2005 CS539: Machine Learning Course Instructor:

Three kinds of learning

1 MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING By Kaan Tariman M.S. in Computer Science CSCI 8810 Course Project.

Introduction to WEKA Aaron 2/13/2009. Contents Introduction to weka Download and install weka Basic use of weka Weka API Survey.

General Mining Issues a.j.m.m. (ton) weijters Overfitting Noise and Overfitting Quality of mined models (some figures are based on the ML-introduction.

Introduction to Data Mining Engineering Group in ACL.

Machine Learning CS 165B Spring 2012

Attention Deficit Hyperactivity Disorder (ADHD) Student Classification Using Genetic Algorithm and Artificial Neural Network S. Yenaeng 1, S. Saelee 2.

JM - 1 Introduction to Bioinformatics: Lecture VIII Classification and Supervised Learning Jarek Meller Jarek Meller Division.

Data Mining Chun-Hung Chou

Engineering Applications of Artificial Intelligence,

Dr M F Abbod Using Intelligent Optimisation Methods to Improve the Group Method of Data Handling in Time Series Prediction Maysam Abbod and Karishma Dashpande.

A Genetic Algorithms Approach to Feature Subset Selection Problem by Hasan Doğu TAŞKIRAN CS 550 – Machine Learning Workshop Department of Computer Engineering.

Predicting Income from Census Data using Multiple Classifiers Presented By: Arghya Kusum Das Arnab Ganguly Manohar Karki Saikat Basu Subhajit Sidhanta.

Essay Outline Quick and Easy Guide to Writing a Correct Essay.

Breeding Decision Trees Using Evolutionary Techniques Papagelis Athanasios - Kalles Dimitrios Computer Technology Institute & AHEAD RM.

An Example of Course Project Face Identification.

Ensemble of ensemble of tree and neural network Louis Duclos-Gosselin.

GA-Based Feature Selection and Parameter Optimization for Support Vector Machine Cheng-Lung Huang, Chieh-Jen Wang Expert Systems with Applications, Volume.

GATree: Genetically Evolved Decision Trees 전자전기컴퓨터공학과 데이터베이스 연구실 G 김태종.

Categorical data. Decision Tree Classification Which feature to split on? Try to classify as many as possible with each split (This is a good split)

Data Mining Teaching experience at the FIB. What is Data Mining? A broad set of techniques and algorithms brought from machine learning and statistics.

Classification Techniques: Bayesian Classification

Multi-Relational Data Mining: An Introduction Joe Paulowskey.

Taguchi. Abstraction Optimisation of manufacturing processes is typically performed utilising mathematical process models or designed experiments. However,

CS690L Data Mining: Classification

Using Machine Learning Techniques in Stylometry Ramyaa, Congzhou He, Dr. Khaled Rasheed.

Improving the Genetic Algorithm Performance in Aerial Spray Deposition Management University of Georgia L. Wu, W.D. Potter, K. Rasheed USDA Forest Service.

1 CSCI 3202: Introduction to AI Decision Trees Greg Grudic (Notes borrowed from Thomas G. Dietterich and Tom Mitchell) Intro AIDecision Trees.

October 2-3, 2015, İSTANBUL Boğaziçi University Prof.Dr. M.Erdal Balaban Istanbul University Faculty of Business Administration Avcılar, Istanbul - TURKEY.

Learning Classifier Systems (Introduction) Muhammad Iqbal Evolutionary Computation Research Group School of Engineering and Computer Science Victoria University.

Text Document Categorization by Term Association Maria-luiza Antonie Osmar R. Zaiane University of Alberta, Canada 2002 IEEE International Conference on.

Classification using Decision Trees 1.Data Mining and Information 2.Data Mining and Machine Learning Techniques 3.Decision trees and C5 4.Applications.

Data Mining and Decision Support

2002/1/17IDS Lab Seminar Evaluating a clustering solution: An application in the tourism market Advisor: Dr. Hsu Graduate: Yung-Chu Lin.

Regression Methods. Linear Regression  Simple linear regression (one predictor)  Multiple linear regression (multiple predictors)  Ordinary Least Squares.

Il-Ahn Cheong Linux Security Research Center Chonnam National University, Korea.

Introduction to Machine Learning, its potential usage in network area,

Jonatas Wehrmann, Willian Becker, Henry E. L. Cagnini, and Rodrigo C

Classification with Gene Expression Data

Debesh Jha and Kwon Goo-Rak

Prepared by: Mahmoud Rafeek Al-Farra

A Methodology for Finding Bad Data

SMA5422: Special Topics in Biotechnology

Implementing Boosting and Convolutional Neural Networks For Particle Identification (PID) Khalid Teli .

Predict House Sales Price

Decision Trees Greg Grudic

Classification Techniques: Bayesian Classification

Machine Learning Today: Reading: Maria Florina Balcan

Dr. Morgan C. Wang Department of Statistics

Prepared by: Mahmoud Rafeek Al-Farra

Prepared by: Mahmoud Rafeek Al-Farra

CS539: Project 3 Zach Pardos.

Predicting Student Performance: An Application of Data Mining Methods with an Educational Web-based System FIE 2003, Boulder, Nov 2003 Behrouz Minaei-Bidgoli,

Behrouz Minaei, William Punch

Deep Forest: Towards an Alternative to Deep Neural Networks

CSE 573 Introduction to Artificial Intelligence Decision Trees

Discriminative Frequent Pattern Analysis for Effective Classification

MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING

MACHINE LEARNING TECHNIQUES IN IMAGE PROCESSING

Lecture 4. Niching and Speciation (1)

Regression Methods.

Using Bayesian Network in the Construction of a Bi-level Multi-classifier. A Case Study Using Intensive Care Unit Patients Data B. Sierra, N. Serrano,

Advisor: Dr.vahidipour Zahra salimian Shaghayegh jalali Dec 2017

Presenter: Donovan Orn

Presentation transcript:

Machine Learning Techniques for the Evaluating of External Skeletal Fixation Structure Dr.Khaled Rasheed Dr. Walter D. Potter Dr. Dennis N. Aron Ning Suo

Presentation Outline Introduction --- Background --- Related Works Prior Analysis Methods proposed Experiment and Result Future Directions Questions & Suggestions

Introduction Fact Related work * Accident * External fixation * 136,437,480 pets * Accident * External fixation * What we can help Related work * "Bone-FixES" * Decision Tree

Prior Analysis Data 12 patients; 5 treatment proposal; 35 parameters Methods ---Full parameters approach vs. Reduced Parameters * Full parameters: 35 parameters * Reduced parameters: 4 parameters ---Binary Prediction vs. Multiple Class Prediction * Binary prediction : Preprocessing data i.e. 1,2,3  0 * Multiple prediction: Score this treatment

Proposed Methods ? Genetic Algorithm Set ANN Structure Decision Tree Classifier System Artificial Neural Network Genetic Algorithm Set ANN Structure

Methods I--Decision Tree What is it? * Inductive learning, positive and negative examples * If then rules * Heuristic method Featured operators * Cross-validation, boosting, pruning, winnowing attributes Program C5.0 by Ross Quinlan i.e. if KE ^ (pin_num>4) ^ (Duration_surgery<3) ^ (close)^ (frame_num>5) then score 8

Method II-Classifier System What is it? * Machine learning system * Learn rules and guide its performance Components

Method III- ANN Three Layer backpropgation Neural Network Momentum and Learning Rate Spread

Method IV-G.A.A.N.N. Generational GA Representation Selection Method Genetic Operator Fitness Stop Criteria

Experiment Result 1. Decision Tree: Correct Rate=66.66% Full Data with Multi-class Prediction Reduced Data with Multi-class Prediction Correct Rate=66.66% Correct Rate=73.33%

Experiment Result (con’d) 2. Classifier System Classifier System Full Data with Multi-class Prediction Reduced Data with Multi-class Prediction Correct Rate=21% Correct Rate=44%

Experiment Result (con’d) 3. Artificial Neural Network Artificial Neural Network Full Data with Multi-class Prediction Reduced Data with Multi-class Prediction Correct Rate=31.7% Correct Rate=45%

Experiment Result (con’d) 4. GAANN GAANN Full Data with Multi-class Prediction Reduced Data with Multi-class Prediction Correct Rate=63.3% Correct Rate=53.3%

Experiment Result (con’d) Decision Tree Classifier System GAANN Artificial Neural Network GAANN

Conclusion Decision tree did very well since boosting and cross validation techniques were applied. GAANN shows more potential. A GA with more features such as special operators will performs better. Data set too small.

Note Special Thanks: FPAS Web Page Comments Dr. Ron McClendon Marc Schenkel Jaymin Kessler Jason Schlachter FPAS Web Page Comments