Presentation is loading. Please wait.

Presentation is loading. Please wait.

Ensemble methods with Data Streams

Published byBrendan Barnett Modified over 6 years ago

Similar presentations

Presentation on theme: "Ensemble methods with Data Streams"— Presentation transcript:

1 Ensemble methods with Data Streams
Jungbeom Lee CS240B

2 Outline Intro Ensemble in Machine learning Online ensemble algorithms
Future work

3 Intro Previous class: Data Streams Classifiers Ensemble methods
Online algorithm

4 Classifiers The batch classification problem:
Given a finite training set D={(x,y)} , where y={y1, y2, …, yk}, |D|=n, find a function y=f(x) that can predict the y value for an unseen instance x The data stream classification problem: Given an infinite sequence of pairs of the form (x,y) where y={y1, y2, …, yk}, find a function y=f(x) that can predict the y value for an unseen instance x Example applications: Fraud detection in credit card transactions Topic classification in a news aggregation site, e.g. Google news Translator for foreign languages Supervised learning

5 Motivations Data Volume Changing data characteristics Cost of Learning
Online mining different from static mining Data Volume impossible to mine the entire data at one time can only afford constant memory per data sample Changing data characteristics previously learned models are invalid Cost of Learning model updates can be costly can only afford constant time per data sample.

6 Ensemble A set of classifiers whose individual decisions are combined in some way to classify new examples An ensemble of classifiers to be more accurate than any of its individual members one key to successful is to use individual classifiers with error rates below .5

7 Reasons

8 Ensemble methods Manipulating the Training Examples
Bagging Adaboost Injecting Randomness C4.5 decision tree algorithm

9 Bagging algorithm

10 Bagging algorithm

11 Online bagging algorithm

12 Online weighted bagging algorithm

13 AdaBoost algorithm

14 AdaBoost algorithm

15 Adaptive boosting algorithm

16 Experimental Results

17 Type of Data

18 Experimental Results

19 Experimental Results

20 Experimental Results

21 Future work Better online algorithm for Bagging
Dealing with multiple data types

22 References /mcs-ensembles.pdf A2008.pdf W77MW0J8CP.pdf archive/archive/0962.pdf df

Download ppt "Ensemble methods with Data Streams"

Similar presentations

A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions Jing Gao Wei Fan Jiawei Han Philip S. Yu University of Illinois.

A General Framework for Mining Concept-Drifting Data Streams with Skewed Distributions Jing Gao Wei Fan Jiawei Han Philip S. Yu University of Illinois.
Is Random Model Better? -On its accuracy and efficiency-

Is Random Model Better? -On its accuracy and efficiency-
Data Mining and Machine Learning

Data Mining and Machine Learning
My name is Dustin Boswell and I will be presenting: Ensemble Methods in Machine Learning by Thomas G. Dietterich Oregon State University, Corvallis, Oregon.

My name is Dustin Boswell and I will be presenting: Ensemble Methods in Machine Learning by Thomas G. Dietterich Oregon State University, Corvallis, Oregon.
Boosting Rong Jin.

Boosting Rong Jin.
© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/2004 1 Other Classification Techniques 1.Nearest Neighbor Classifiers 2.Support Vector Machines.

© Tan,Steinbach, Kumar Introduction to Data Mining 4/18/ Other Classification Techniques 1.Nearest Neighbor Classifiers 2.Support Vector Machines.
Data Mining Classification: Alternative Techniques

Data Mining Classification: Alternative Techniques
Ensemble Methods An ensemble method constructs a set of base classifiers from the training data Ensemble or Classifier Combination Predict class label.

Ensemble Methods An ensemble method constructs a set of base classifiers from the training data Ensemble or Classifier Combination Predict class label.
Data Mining Classification: Alternative Techniques

Data Mining Classification: Alternative Techniques
Longin Jan Latecki Temple University

Longin Jan Latecki Temple University
Introduction to Boosting Slides Adapted from Che Wanxiang( 车 万翔 ) at HIT, and Robin Dhamankar of Many thanks!

Introduction to Boosting Slides Adapted from Che Wanxiang( 车 万翔 ) at HIT, and Robin Dhamankar of Many thanks!
UCI KDD Archive University of California at Irvine –

UCI KDD Archive University of California at Irvine –
Model Evaluation Metrics for Performance Evaluation

Model Evaluation Metrics for Performance Evaluation
Sparse vs. Ensemble Approaches to Supervised Learning

Sparse vs. Ensemble Approaches to Supervised Learning
On Appropriate Assumptions to Mine Data Streams: Analyses and Solutions Jing Gao† Wei Fan‡ Jiawei Han† †University of Illinois at Urbana-Champaign ‡IBM.

On Appropriate Assumptions to Mine Data Streams: Analyses and Solutions Jing Gao† Wei Fan‡ Jiawei Han† †University of Illinois at Urbana-Champaign ‡IBM.
2D1431 Machine Learning Boosting.

2D1431 Machine Learning Boosting.
Boosting Rong Jin. Inefficiency with Bagging D Bagging … D1D1 D2D2 DkDk Boostrap Sampling h1h1 h2h2 hkhk Inefficiency with boostrap sampling: Every example.

Boosting Rong Jin. Inefficiency with Bagging D Bagging … D1D1 D2D2 DkDk Boostrap Sampling h1h1 h2h2 hkhk Inefficiency with boostrap sampling: Every example.
Ensemble Learning: An Introduction

Ensemble Learning: An Introduction
Adaboost and its application

Adaboost and its application
Examples of Ensemble Methods

Examples of Ensemble Methods

Similar presentations

Ads by Google