Artificial Intelligence Project #3 : Analysis of Decision Tree Learning Using WEKA May 23, 2006.

Slides:



Advertisements
Similar presentations
1 Machine Learning: Lecture 3 Decision Tree Learning (Based on Chapter 3 of Mitchell T.., Machine Learning, 1997)
Advertisements

Decision Trees Decision tree representation ID3 learning algorithm
1er. Escuela Red ProTIC - Tandil, de Abril, Decision Tree Learning 3.1 Introduction –Method for approximation of discrete-valued target functions.
Decision Tree Algorithm (C4.5)
ICS320-Foundations of Adaptive and Learning Systems
Classification Techniques: Decision Tree Learning
Decision Tree Learning 主講人:虞台文 大同大學資工所 智慧型多媒體研究室.
Decision Tree Learning
Knowledge Representation. 2 Outline: Output - Knowledge representation  Decision tables  Decision trees  Decision rules  Rules involving relations.
Microarray GEO – Microarray sets database
Machine Learning II Decision Tree Induction CSE 473.
Decision Tree Learning
Part 7.3 Decision Trees Decision tree representation ID3 learning algorithm Entropy, information gain Overfitting.
Lazy Associative Classification By Adriano Veloso,Wagner Meira Jr., Mohammad J. Zaki Presented by: Fariba Mahdavifard Department of Computing Science University.
CS 590M Fall 2001: Security Issues in Data Mining Lecture 4: ID3.
Decision Tree Learning Learning Decision Trees (Mitchell 1997, Russell & Norvig 2003) –Decision tree induction is a simple but powerful learning paradigm.
Induction of Decision Trees
Decision Trees and Decision Tree Learning Philipp Kärger
Decision Trees Decision tree representation Top Down Construction
Ch 3. Decision Tree Learning
Decision Tree Pruning Methods Validation set – withhold a subset (~1/3) of training data to use for pruning –Note: you should randomize the order of training.
Artificial Intelligence Term Project #3 Kyu-Baek Hwang Biointelligence Lab School of Computer Science and Engineering Seoul National University
Machine Learning Lecture 10 Decision Trees G53MLE Machine Learning Dr Guoping Qiu1.
Chapter 7 Decision Tree.
Decision tree learning
By Wang Rui State Key Lab of CAD&CG
Machine Learning Chapter 3. Decision Tree Learning
Short Introduction to Machine Learning Instructor: Rada Mihalcea.
Artificial Intelligence 7. Decision trees
Mohammad Ali Keyvanrad
Decision tree learning Maria Simi, 2010/2011 Inductive inference with decision trees  Decision Trees is one of the most widely used and practical methods.
Machine Learning Lecture 10 Decision Tree Learning 1.
CpSc 810: Machine Learning Decision Tree Learning.
Basic Data Mining Technique
1 Knowledge Discovery Transparencies prepared by Ho Tu Bao [JAIST] ITCS 6162.
Decision Tree Learning
Classification and Prediction Compiled By: Umair Yaqub Lecturer Govt. Murray College Sialkot Readings: Chapter 6 – Han and Kamber.
CS690L Data Mining: Classification
CS 8751 ML & KDDDecision Trees1 Decision tree representation ID3 learning algorithm Entropy, Information gain Overfitting.
MACHINE LEARNING 10 Decision Trees. Motivation  Parametric Estimation  Assume model for class probability or regression  Estimate parameters from all.
Artificial Intelligence Project #3 : Diagnosis Using Bayesian Networks May 19, 2005.
Decision Tree Learning
Decision Tree Learning Presented by Ping Zhang Nov. 26th, 2007.
Seminar on Machine Learning Rada Mihalcea Decision Trees Very short intro to Weka January 27, 2003.
1 Classification: predicts categorical class labels (discrete or nominal) classifies data (constructs a model) based on the training set and the values.
Data Mining By Farzana Forhad CS 157B. Agenda Decision Tree and ID3 Rough Set Theory Clustering.
Machine Learning Recitation 8 Oct 21, 2009 Oznur Tastan.
Outline Decision tree representation ID3 learning algorithm Entropy, Information gain Issues in decision tree learning 2.
Friday’s Deliverable As a GROUP, you need to bring 2N+1 copies of your “initial submission” –This paper should be a complete version of your paper – something.
1 By: Ashmi Banerjee (125186) Suman Datta ( ) CSE- 3rd year.
Decision Tree Pruning problem of overfit approaches
DATA MINING TECHNIQUES (DECISION TREES ) Presented by: Shweta Ghate MIT College OF Engineering.
Review of Decision Tree Learning Bamshad Mobasher DePaul University Bamshad Mobasher DePaul University.
Data Mining Practical Machine Learning Tools and Techniques Chapter 6.3: Association Rules Rodney Nielsen Many / most of these slides were adapted from:
CSE573 Autumn /11/98 Machine Learning Administrative –Finish this topic –The rest of the time is yours –Final exam Tuesday, Mar. 17, 2:30-4:20.
DECISION TREES An internal node represents a test on an attribute.
Università di Milano-Bicocca Laurea Magistrale in Informatica
CS 9633 Machine Learning Decision Tree Learning
Decision Tree Learning
Decision trees (concept learnig)
Machine Learning Lecture 2: Decision Tree Learning.
Decision trees (concept learnig)
Prepared by: Mahmoud Rafeek Al-Farra
Decision Tree Saed Sayad 9/21/2018.
Machine Learning Chapter 3. Decision Tree Learning
Machine Learning: Lecture 3
Decision Trees Decision tree representation ID3 learning algorithm
Machine Learning Chapter 3. Decision Tree Learning
Decision Trees Decision tree representation ID3 learning algorithm
INTRODUCTION TO Machine Learning
Presentation transcript:

Artificial Intelligence Project #3 : Analysis of Decision Tree Learning Using WEKA May 23, 2006

Introduction Decision tree learning is a method for approximating discrete-valued target function The learned function is represented by a decision tree Decision tree can also be re-represented as if-then rules to improve human readability

An Example of Decision Tree

Decision Tree Representation (1/2) Decision tree classify instances by sorting them down the tree from the root to some leaf node Node Specifies test of some attribute Branch Corresponds to one of the possible values for this attribute

Decision Tree Representation (2/2) Each path corresponds to a conjunction of attribute tests (Outlook=sunny, Temperature=Hot, Humidity=high, Wind=Strong)  (Outlook=Sunny ∧ Humidity=High) so NO Decision trees represent a disjunction of conjunction of constraints on the attribute values of instances ( Outlook=Sunny ∧ Humidity=normal) ∨ (Outlook=Overcast) ∨ (Outlook=Rain ∧ Wind=Weak) Outlook Humidity Yes No Yes Wind NoYes Sunny Overcast Rain High NormalStrongWeak What is the merit of tree representation?

Appropriate Problems for Decision Tree Learning Instances are represented by attribute-value pairs The target function has discrete output values Disjunctive descriptions may be required The training data may contain errors Both errors in classification of the training examples and errors in the attribute values The training data may contain missing attribute values Suitable for classification

Study Treatment-specific changes in gene expression discriminate in vivo drug response in human leukemia cells, MH Cheok et al., Nature Genetics 35, leukemia patients Bone marrow samples Affymetrix GeneChip arrays Gene expression data

Gene Expression Data # of data examples 120 (60: before treatment, 60: after treatment) # of genes measured (Affymetrix HG-U95A array) Task Classification between “before treatment” and “after treatment” based on gene expression pattern

Affymetrix GeneChip Arrays Use short oligos to detect gene expression level. Each gene is probed by a set of short oligos. Each gene expression level is summarized by Signal: numerical value describing the abundance of mRNA A/P call: denotes the statistical significance of signal

Preprocessing Remove the genes having more than 60 ‘A’ calls # of genes:  3190 Discretization of gene expression level Criterion: median gene expression value of each sample 0 (low) and 1 (high)

Gene Filtering Using mutual information Estimated probabilities were used. # of genes: 3190  1000 Final dataset # of attributes: 1001 (one for the class) Class: 0 (after treatment), 1 (before treatment) # of data examples: 120

Final Dataset

Materials for the Project Given Preprocessed microarray data file: data2.txt Downloadable WEKA (

Analysis of Decision Tree Learning

Submission Due date: June 15 (Thu.), 12:00(noon) Report: Hard copy( ) & . ID3, J48 and another decision tree algorithm with learning parameter. Show the experimental results of each algorithm. Except for ID3, you should try to find out better performance, changing learning parameter. Analyze what makes difference between selected algorithms.