1,720,956 research outputs found
On the reliability of the area under the ROC curve in empirical software engineering
No full textBinary classifiers are commonly used in software engineering research to estimate several software qualities, e.g., defectiveness or vulnerability. Thus, it is important to adequately evaluate how well binary classifiers perform, before they are used in practice. The Area Under the Curve (AUC) of Receiver Operating Characteristic curves has often been used to this end. However, AUC has been the target of some criticisms, so it is necessary to evaluate under what conditions and to what extent AUC can be a reliable performance metric.
We analyze AUC in relation to φ (also known as Matthews Correlation Coefficient), often considered a more reliable performance metric, by building the lines in the ROC space with constant value of φ, for several values of φ, and computing the corresponding values of AUC.
By their very definitions, AUC and φ depend on the prevalence ρ of a dataset, which is the proportion of its positive instances (e.g., the defective software modules). Hence, so does the relationship between AUC and φ. It turns out that AUC and φ are very well correlated, and therefore provide concordant indications, for balanced datasets (those with ρ ≃ 0.5). Instead, AUC tends to become quite large, and hence provide over-optimistic indications, for very imbalanced datasets (those with ρ ≃ 0 or ρ ≃ 1).
We use examples from the software engineering literature to illustrate the analytical relationship linking AUC, φ, and ρ. We show that, for some values of ρ, the evaluation of performance based exclusively on AUC can be deceiving. In conclusion, this paper provides some guidelines for an informed usage and interpretation of AUC
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
Software Defect Prediction evaluation: New metrics based on the ROC curve
No full textContext: ROC (Receiver Operating Characteristic) curves are widely used to represent how well fault-proneness models (e.g., probability models) classify software modules as faulty or non-faulty. AUC, the Area Under the ROC Curve, is usually used to quantify the overall discriminating power of a fault-proneness model. Alternative indicators proposed, e.g., RRA (Ratio of Relevant Areas), consider the area under a portion of a ROC curve. Each point of a ROC curve represents a binary classifier, obtained by setting a specified threshold on the fault-proneness model. Several performance metrics (Precision, Recall, the F-score, etc.) are used to assess a binary classifier. Objectives: We investigate the relationships linking ‘‘under the ROC curve area’’ indicators such as AUC and RRA to performance metrics. Methods: We study these relationships analytically. We introduce iso-PM ROC curves, whose points have the same value isoPM for a given performance metric PM. When evaluating a ROC curve, we identify the iso-PM curve with the same value of AUC or RRA. Its isoPM can be seen as a property of the ROC curve and fault-proneness model under evaluation. Results: There is an S-shaped relationship between isoPM and AUC for performance metrics that do not depend on the proportion ρ of faulty modules, i.e., dataset balancedness. φ (Matthews Correlation Coefficient) depends on ρ: with very imbalanced datasets, AUC appears over-optimistic and φ over-pessimistic. RRA defines the region of interest in terms of ρ, so all performance metrics depend on ρ. RRA is related to performance metrics via S-shaped curves. Conclusion: Our proposal helps gain a better quantitative understanding of the goodness of a ROC curve, especially in practically relevant regions of interest. Also, showing a ROC curve and iso-PM curves provides an intuitive perception of the goodness of a fault-proneness model
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
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