How To Calculate Apparent Error Rate
error rate in the case of multi-group classification? i already know for the case of two groups/populations. The question is, what will be the Actual Error Rate formula for evaluating the performance of a classifier for a three group case. Topics Survey Methodology and Data Analysis × 375 Questions 8,782 Followers Follow Evaluation × 362 Questions 1,059 Followers Follow Classifier × 308 Questions 42 Followers Follow Jan 6, 2016 Share Facebook Twitter LinkedIn Google+ 0 / 0 All Answers (2) Michael Asamoah-Boaheng · Kumasi Polytechnic Thanks Jan 23, 2016 Can you help by adding an answer? Add your answer Question followers (4) Jenkins Macedo Clark University Andreas Theissler Robert Bosch GmbH Michael Asamoah-Boaheng Kumasi Polytechnic Daniel Ren University of Auckland Views 142 Followers 4 Answers 2 © 2008-2016 researchgate.net. All rights reserved.About us · Contact us · Careers · Developers · News · Help Center · Privacy · Terms · Copyright | Advertising · Recruiting We use cookies to give you the best possible experience on ResearchGate. Read our cookies policy to learn more.OkorDiscover by subject areaRecruit researchersJoin for freeLog in EmailPasswordForgot password?Keep me logged inor log in with ResearchGate is the professional network for scientists and researchers. Got a question you need answered quickly? Technical questions like the one you've just found usually get answered within 48 hours on ResearchGate. Sign up today to join our community of over 10+ million scientific professionals. Join for free An error occurred while rendering template. rgreq-e70a4fd849de4ab01f6d80b697f36589 false
here for a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Stack Overflow Questions Jobs Documentation Tags Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 4.7 million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up How to calculate classification error rate up vote 1 down vote favorite Alright. Now this question is pretty hard. I am going to give you an example. Now the left numbers are my algorithm classification and the right numbers are the original class numbers 177 86 177 86 177 86 https://www.researchgate.net/post/Can_anyone_help_me_with_the_formula_for_Actual_Apparent_error_rate_in_the_case_of_multi-group_classification 177 86 177 86 177 86 177 86 177 86 177 86 177 89 177 89 177 89 177 89 177 89 177 89 177 89 So here my algorithm merged 2 different classes into 1. As you can see it merged class 86 and 89 into one class. So what would be the error at the above example ? Or here another example 203 7 203 7 203 7 203 7 16 7 203 7 17 7 16 7 203 7 At the above example left numbers http://stackoverflow.com/questions/10067118/how-to-calculate-classification-error-rate are my algorithm classification and the right numbers are original class ids. As can be seen above it miss classified 3 products (i am classifying same commercial products). So at this example what would be the error rate? How would you calculate. This question is pretty hard and complex. We have finished the classification but we are not able to find correct algorithm for calculating success rate :D algorithm cluster-analysis classification ratio confusion-matrix share|improve this question edited May 11 '13 at 16:26 denis 10.6k43856 asked Apr 8 '12 at 22:36 MonsterMMORPG 6,20141121222 add a comment| 4 Answers 4 active oldest votes up vote 2 down vote accepted Here's a longish example, a real confuson matrix with 10 input classes "0" - "9" (handwritten digits), and 10 output clusters labelled A - J. Confusion matrix for 5620 optdigits: True 0 - 9 down, clusters A - J across ----------------------------------------------------- A B C D E F G H I J ----------------------------------------------------- 0: 2 4 1 546 1 1: 71 249 11 1 6 228 5 2: 13 5 64 1 13 1 460 3: 29 2 507 20 5 9 4: 33 483 4 38 5 3 2 5: 1 1 2 58 3 480 13 6: 2 1 2 294 1 1 257 7: 1 5 1 546 6 7 8: 415 15 2 5 3 12 13 87 2 9: 46 72 2 357 35 1 47 2 ---------------------------------------------------- 580 383 496 1002 307 670 549 557 810 266 estimates in each cluster y class sizes: [554 571 557 572 568 558 55
institution loginHelpJournalsBooksRegisterJournalsBooksRegisterSign inHelpcloseSign in using your ScienceDirect credentialsUsernamePasswordRemember meForgotten username or password?Sign in via your institutionOpenAthens loginOther institution login Download http://www.sciencedirect.com/science/article/pii/0898122186900787 full text in PDF Article Article + other articles in this issue Loading... Export You have selected 1 citation for export. Help Direct export Save to Mendeley Save to RefWorks Export file Format RIS (for EndNote, ReferenceManager, ProCite) BibTeX Text Content Citation Only Citation and Abstract how to Export Advanced search Close This document does not have an outline. JavaScript is disabled on your browser. Please enable JavaScript to use all the features on this page. Computers & Mathematics with Applications Volume 12, Issue 2, Part A, February 1986, Pages 253-260 The robust estimation of how to calculate classification error rates Author links open the overlay panel. Numbers correspond to the affiliation list which can be exposed by using the show more link. Opens overlay James D. Knoke Department of Biostatistics, University of North Carolina, Chapel Hill, NC 27514, U.S.A. Available online 4 September 2002 Show more doi:10.1016/0898-1221(86)90078-7 Get rights and content Under an Elsevier user license Open Archive AbstractRecent work on robust error rate estimation in classification analysis is summarized. First, the perspective for the error rate estimation problem is established, and the parameters that are referred to as error rates are described. Next, the bases for comparison of error rate estimators are reviewed and a mean-square error criterion recommended. Then several approaches to robust error rate estimation are introduced. Finally, recommendations for applications based on available evaluations of robust estimators are made, and important un
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