Selection Regions Assume we have a set of classifiers D = { D 1, D - PDF document

Mar 17, 2023 •423 likes •466 views

Selection Regions Assume we have a set of classifiers D = { D 1, D 2, ..., D L } Let R n be divided into K selection regions (also called regions of competence ) called {R 1, R 2, ..., R k } Let E map each input x to its corresponding Region R j E

Selection Regions Assume we have a set of classifiers D = { D 1, D 2, ..., D L } Let R n be divided into K selection regions (also called regions of competence ) called {R 1, R 2, ..., R k } Let E map each input x to its corresponding Region R j E : x → R j , where R j is the region for which D i ( j ) is applied Feed x into D i ( j ) iff E( x ) = R j Note: Combination for this definition is trivial (it forwards the one classification that it receives), but may be used in extensions that require fusion. Competence Estimation Decision-independent vs. decision-dependent: whether or not label chosen by classifiers are known Direct k-nn: Decision independent: calculate accuracy of classifiers on k nearest neighbors of input x. Decision dependent: determine k nearest neighbors of x given the same label as x . Competence is the accuracy of the classifier in these nearest neighbors. Distance-based k-nn: uses confidence measure output by the classifier Decision independent: weighted average of classifier outputs for each correct label in the set of neighbors
Decision dependent: weighted average of classifier outputs for neighbors whose true class label is the same as that chosen for the input Potential functions: Points contribute positively to a classifier's potential if correctly recognized and negatively otherwise. This potential field is weighted by the distance from the point to the input element. , Pre-estimation of Competence Regions K = number of regions of competence L = number of classifiers Decide a classifier from D = {D 1 , ... D L } for each region R j , j = 1,...K. For input x , find its region of competence and choose most compete classifier for that region (D i(j) ) Selection or Fusion? Run paired t-test to determine statistical significance of classifier D i(j) . If difference in accuracies between best classifier and all other classifiers is significant, use classifier selection. Otherwise, use fusion. P D = Accuracy of classifier D i in region R j t = Statistic with parameters α (level of significance), degrees of freedom (d.o.f.) N = Sample size = 95% (1-.05) Confidence Interval, e.g. = threshold for statistical significance (for N>=30, α = 0.05)

Recommend

Classifier Selection Nicholas Ver Hoeve Craig Martek Ben Gardner Classifier Ensembles Assume

Classifier Selection Nicholas Ver Hoeve Craig Martek Ben Gardner Classifier Ensembles Assume we have an ensemble of classifiers with a well-chosen feature set. We want to optimize the competence of this system. Simple enhancements include:

512 views • 27 slides

Automatic Sample-by- sample Model Selection Between Two Off-the-shelf Classifiers Steve P.

Automatic Sample-by- sample Model Selection Between Two Off-the-shelf Classifiers Steve P. Chadwick University of Texas at Dallas Model Selection by Predicting the Better Classifier Idea: Two classifiers, "primary" and

269 views • 6 slides

Linear Classifiers and the Perceptron William Cohen February 4, 2008 1 Linear classifiers

Linear Classifiers and the Perceptron William Cohen February 4, 2008 1 Linear classifiers Lets assume that every instance is an n -dimensional vector of real numbers x R n , and there are only two possible classes, y = (+1) and y = (

269 views • 9 slides

Steganalysis by Ensemble Classifiers with Boosting by Regression, and Post-Selection of Features

Steganalysis by Ensemble Classifiers - Marc Chaumont - ICIP2012 Steganalysis by Ensemble Classifiers with Boosting by Regression, and Post-Selection of Features Marc Chaumont, Sarra Kouider LIRMM, Montpellier, France October 2, 2012 IEEE

376 views • 26 slides

Nonlinear Classifiers II 2 Nonlinear Classifiers: Introduction Classifiers Supervised

1 Nonlinear Classifiers II 2 Nonlinear Classifiers: Introduction Classifiers Supervised Classifiers XOR problem Linear Classifiers Perceptron Least Squares Methods Linear Support Vector Machine Nonlinear

355 views • 20 slides

Making Generative Classifiers Robust to Selection Bias Andrew Smith Charles Elkan November

Making Generative Classifiers Robust to Selection Bias Andrew Smith Charles Elkan November 30th, 2007 Outline What is selection bias? Types of selection bias. Overcoming learnable bias with weighting. Overcoming bias with

736 views • 59 slides

Neural Network Classifiers and Gene Selection Methods for Microarray Data on Human Lung

Neural Network Classifiers and Gene Selection Methods for Microarray Data on Human Lung Adenocarcinoma Gaolin Zheng School of Computer Science Florida International University, Miami, FL E.O. George Department of Mathematical Sciences,

385 views • 26 slides

CSSE463: Image Recognition Day 31 Today: Bayesian classifiers Questions? Bayesian

CSSE463: Image Recognition Day 31 Today: Bayesian classifiers Questions? Bayesian classifiers Use training data p(x) Assume that you know P(x| w 1 ) probabilities of each feature. If 2 classes: P(x| w 2 ) Classes w 1 and

257 views • 13 slides

Statistical Shape Models Eigenpatches model regions Assume shape is fixed What if it

Statistical Shape Models Eigenpatches model regions Assume shape is fixed What if it isn t? Faces with expression changes, organs in medical images etc Need a method of modelling shape and shape variation Shape Models

271 views • 24 slides

Extending Binary Linear Classification One-Versus-All Classification (OVA) } In the presence of

Binary and Other Classification } We will generally discuss binary classifiers, which divide data into one of two classes } Linear classifiers as presented in last lecture are inherently binary, defining the classes based on two regions,

336 views • 7 slides

Chapter 6: Automatic Classification (Supervised Data Organization) 6.1 Simple Distance-based

Chapter 6: Automatic Classification (Supervised Data Organization) 6.1 Simple Distance-based Classifiers 6.2 Feature Selection 6.3 Distribution-based (Bayesian) Classifiers 6.4 Discriminative Classifiers: Decision Trees 6.5 Discriminative

626 views • 41 slides

Occasion-level Classifiers or Event-level Classifiers? -Evidence from Child Language Acquisition

Occasion-level Classifiers or Event-level Classifiers? -Evidence from Child Language Acquisition Yuan Xiaohe, Zhang Xiaoqian, Luo Yingyi, Hu Jianhua Institute of Linguistics Chinese Academy of Social Sciences Verbal classifiers in Mandarin

823 views • 24 slides

Machine Learning Nave Bayes classifiers Types of classifiers We can divide the large

10-701 Machine Learning Nave Bayes classifiers Types of classifiers We can divide the large variety of classification approaches into three major types 1. Instance based classifiers - Use observation directly (no models) - e.g. K

309 views • 28 slides

The Geographic Regions of the US and NC The Geography and 4 Regions of NC NC Geographic

Unit 1 Flipped Video The Geographic Regions of the US and NC The Geography and 4 Regions of NC NC Geographic Features and Natural Resources Climate REGIONS Tide- Hurricanes, shallow sounds, and Humid Subtropical water the Barrier

702 views • 14 slides

Natural Selection 02-715 Advanced Topics in Computa8onal Genomics

Natural Selection 02-715 Advanced Topics in Computa8onal Genomics Natural Selection Compara8ve studies across species O=en focus on protein-coding regions Genes

754 views • 32 slides

Natural Selection 02-715 Advanced Topics in Computa8onal Genomics

Natural Selection 02-715 Advanced Topics in Computa8onal Genomics Natural Selection Compara8ve studies across species O=en focus on protein-coding regions Genes

450 views • 30 slides

Automatically Evading Classifiers A Case Study on PDF Malware Classifiers Weilin Xu

Automatically Evading Classifiers A Case Study on PDF Malware Classifiers Weilin Xu David Evans Yanjun Qi University of Virginia Machine Learning is Solving Our Problems Fake Fake Spam IDS

1.07k views • 34 slides

CS440/ECE448 Lecture 22: Including Slides by Svetlana Lazebnik, 10/2016 Linear Classifiers

Mark Hasegawa-Johnson, 3/2020 CS440/ECE448 Lecture 22: Including Slides by Svetlana Lazebnik, 10/2016 Linear Classifiers License: CC-BY 4.0 Linear Classifiers Classifiers Perceptron Linear classifiers in general Logistic

581 views • 45 slides

Cognitive Modeling Unseen Examples 2 Bayes Classifiers Lecture 14: Naive Bayes Classifiers

Decision Making as Classification Decision Making as Classification Bayes Classifiers Bayes Classifiers Naive Bayes Classifiers Naive Bayes Classifiers 1 Decision Making as Classification Decision Making Frequencies and Probabilities

269 views • 5 slides

First look at structures CS 6355: Structured Prediction 1 So far Binary classifiers

First look at structures CS 6355: Structured Prediction 1 So far Binary classifiers Output: 0/1 Multiclass classifiers Output: one of a set of labels Linear classifiers for both Learning algorithms Winner-take-all

1.05k views • 75 slides

Combining Classifiers d i,j = 1 if D i labels x in i , and d i,j = 0 otherwise. In this case,

Majority Vote Classifiers output a c-dimensional binary vector [ d i, 1 , ..., d i,c ] T { 0 , 1 } c , where i = 1 , ..., L and Combining Classifiers d i,j = 1 if D i labels x in i , and d i,j = 0 otherwise. In this case, plurality will

630 views • 4 slides

Fusion of Continuous Output Classifiers Classifiers Jacob Hays Amit Pillay James DeFelice

Fusion of Continuous Output Classifiers Classifiers Jacob Hays Amit Pillay James DeFelice Definitions x feature vector c number of classes L number of classifiers { 1 , 2 , ., c } Set of class

668 views • 53 slides

regions and cities the role of the European Committee of the Regions Startup Europe Regions

Encouraging entrepreneurship in EU regions and cities the role of the European Committee of the Regions Startup Europe Regions Network Moving Forward Brussels, 14 June 2017 Bert Kuby Head of Unit ECON Commission European Committee of

124 views • 11 slides

Visualization for Explainable Classifiers Yao MING THE HONG KONG UNIVERSITY OF SCIENCE AND

A Survey on Visualization for Explainable Classifiers Yao MING THE HONG KONG UNIVERSITY OF SCIENCE AND TECHNOLOGY In Intro rodu duction Ex Expl plai ainabl able Cl Classifiers Visualization f for E Explainable Cl Classifiers Conc

965 views • 61 slides