For Publishers
DiscoveryMetadataPeer reviewHostingPublishing
For Researchers
JoinPublishReviewCollect
Register
Blog
About
Search
Advanced search
My ScienceOpen
Search
For Publishers
For Researchers
Blog
About
20
views
4
references
 Top references
cited by
86
    
0 reviews
 Review
0
comments
 Comment
0
recommends
+1 Recommend
0 collections
    0
    shares
      24
      similar
       All similar
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A Comparison of MCC and CEN Error Measures in Multi-Class Prediction

      research-article
      Author(s): * , ,
      Editor(s):
      Publication date (Electronic):
      Journal: PLoS ONE
      Publisher: Public Library of Science

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          We show that the Confusion Entropy, a measure of performance in multiclass problems has a strong (monotone) relation with the multiclass generalization of a classical metric, the Matthews Correlation Coefficient. Analytical results are provided for the limit cases of general no-information (n-face dice rolling) of the binary classification. Computational evidence supports the claim in the general case.

          Related collections

          Most cited references4

          • Record: found
          • Abstract: found
          • Article: not found

          The meaning and use of the area under a receiver operating characteristic (ROC) curve.

          J A Hanley, B J McNeil, Marnix van Holsbeeck (1982)
          A representation and interpretation of the area under a receiver operating characteristic (ROC) curve obtained by the "rating" method, or by mathematical predictions based on patient characteristics, is presented. It is shown that in such a setting the area represents the probability that a randomly chosen diseased subject is (correctly) rated or ranked with greater suspicion than a randomly chosen non-diseased subject. Moreover, this probability of a correct ranking is the same quantity that is estimated by the already well-studied nonparametric Wilcoxon statistic. These two relationships are exploited to (a) provide rapid closed-form expressions for the approximate magnitude of the sampling variability, i.e., standard error that one uses to accompany the area under a smoothed ROC curve, (b) guide in determining the size of the sample required to provide a sufficiently reliable estimate of this area, and (c) determine how large sample sizes should be to ensure that one can statistically detect differences in the accuracy of diagnostic techniques.
          Article 0 comments Cited 3991 times – based on 0 reviews     Review now
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Comparing two K-category assignments by a K-category correlation coefficient.

            J Gorodkin (2004)
            Predicted assignments of biological sequences are often evaluated by Matthews correlation coefficient. However, Matthews correlation coefficient applies only to cases where the assignments belong to two categories, and cases with more than two categories are often artificially forced into two categories by considering what belongs and what does not belong to one of the categories, leading to the loss of information. Here, an extended correlation coefficient that applies to K-categories is proposed, and this measure is shown to be highly applicable for evaluating prediction of RNA secondary structure in cases where some predicted pairs go into the category "unknown" due to lack of reliability in predicted pairs or unpaired residues. Hence, predicting base pairs of RNA secondary structure can be a three-category problem. The measure is further shown to be well in agreement with existing performance measures used for ranking protein secondary structure predictions. Server and software is available at http://rk.kvl.dk/.
            Article 0 comments Cited 136 times – based on 0 reviews     Review now
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Evaluating diagnostic tests: The area under the ROC curve and the balance of errors.

              David Hand (2010)
              Because accurate diagnosis lies at the heart of medicine, it is important to be able to evaluate the effectiveness of diagnostic tests. A variety of accuracy measures are used. One particularly widely used measure is the AUC, the area under the receiver operating characteristic (ROC) curve. This measure has a well-understood weakness when comparing ROC curves which cross. However, it also has the more fundamental weakness of failing to balance different kinds of misdiagnoses effectively. This is not merely an aspect of the inevitable arbitrariness in choosing a performance measure, but is a core property of the way the AUC is defined. This property is explored, and an alternative, the H measure, is described. (c) 2010 John Wiley & Sons, Ltd.
              Article 0 comments Cited 22 times – based on 0 reviews     Review now
                Bookmark
                All references

                Author and article information

                Contributors
                : Role: Editor
                Journal
                Journal ID (nlm-ta): PLoS One
                Journal ID (iso-abbrev): PLoS ONE
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosone
                Title: PLoS ONE
                Publisher: Public Library of Science (San Francisco, USA )
                ISSN (Electronic): 1932-6203
                Publication date Collection: 2012
                Publication date (Electronic): 8 August 2012
                Volume: 7
                Issue: 8
                Electronic Location Identifier: e41882
                Affiliations
                [1]Fondazione Bruno Kessler, Trento, Italy
                Sapienza University of Rome, Italy
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: GJ SR. Performed the experiments: GJ SR. Analyzed the data: GJ SR. Contributed reagents/materials/analysis tools: GJ SR. Wrote the paper: GJ CF SR.

                Article
                Publisher ID: PONE-D-12-03597
                DOI: 10.1371/journal.pone.0041882
                PMC ID: 3414515
                PubMed ID: 22905111
                SO-VID: 438a1721-7316-4f00-b139-f853a887e066
                Copyright statement: Copyright @ 2012
                License:

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Date received : 31 January 2012
                Date accepted : 27 June 2012
                Page count
                Pages: 8
                Funding
                The authors acknowledge funding by the European Union FP7 Project HiperDART and by the PAT funded Project ENVIROCHANGE. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Research Article
                Subject: Biology
                Subject: Computational Biology
                Subject: Computer Science
                Subject: Algorithms
                Subject: Computer Applications
                Subject: Mathematics
                Subject: Mathematical Computing
                Subject: Probability Theory
                Subject: Statistical Distributions
                Subject: Statistical Medians
                Subject: Statistics
                Subject: Biostatistics
                Subject: Confidence Intervals
                Subject: Contingency Tables
                Subject: Statistical Methods

                ScienceOpen disciplines: Uncategorized
                Data availability:
                ScienceOpen disciplines: Uncategorized

                Comments

                Comment on this article

                scite_

                Similar content24

                See all similar

                Cited by83

                See all cited by

                Most referenced authors202

                See all reference authors