1,721,261 research outputs found
[Woman in a garden]
No full textMedium: pencildrawingssigned."[Woman in a garden]" [2021.0051.000.000], Granger, E.Artist and Role: Granger, E.,Extent: sightExtent: fram
F-measure curves: a tool to visualize classifier performance under imbalance
Full text linkLearning from imbalanced data is a challenging problem in many real-world machine learning applications due in part to the bias of performance in most classification systems. This bias may exist due to three reasons: (1) Classification systems are often optimized and compared using performance measurements that are unsuitable for imbalance problems; (2) most learning algorithms are designed and tested on a fixed imbalance level of data, which may differ from operational scenarios; (3) the preference of correct classification of classes is different from one application to another. This paper investigates specialized performance evaluation metrics and tools for imbalance problem, including scalar metrics that assume a given operating condition (skew level and relative preference of classes), and global evaluation curves or metrics that consider a range of operating conditions. We focus on the case in which the scalar metric F-measure is preferred over other scalar metrics, and propose a new global evaluation space for the F-measure that is analogous to the cost curves for expected cost. In this space, a classifier is represented as a curve that shows its performance over all of its decision thresholds and a range of possible imbalance levels for the desired preference of true positive rate to precision. Curves obtained in the F-measure space are compared to those of existing spaces (ROC, precision-recall and cost) and analogously to cost curves. The proposed F-measure space allows to visualize and compare classifiers’ performance under different operating conditions more easily than in ROC and precision-recall spaces. This space allows us to set the optimal decision threshold of a soft classifier and to select the best classifier among a group. This space also allows to empirically improve the performance obtained with ensemble learning methods specialized for class imbalance, by selecting and combining the base classifiers for ensembles using a modified version of the iterative Boolean combination algorithm that is optimized using the F-measure instead of AUC. Experiments on a real-world dataset for video face recognition show the advantages of evaluating and comparing different classifiers in the F-measure space versus ROC, precision-recall, and cost spaces. In addition, it is shown that the performance evaluated using the F-measure of Bagging ensemble method can improve considerably by using the modified iterative Boolean combination algorithm
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
A dual-staged classification-selection approach for automated update of biometric templates
No full textIn the emerging field of adaptive biometrics, systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due to limitations associated with existing adaptation schemes. This paper proposes a dual-staged template adaptation scheme that allows to capture `informative' operational samples with significant variations but without increasing the vulnerability to impostor intrusion. This is achieved through a two staged classification-selection approach driven by the harmonic function and risk minimization technique, over a graph based representation of (enrolment and operational) samples. Experimental results on the DIEE fingerprint data set, explicitly collected for evaluating adaptive biometric systems, demonstrate that the proposed scheme results in 67% reduction in error over the baseline system (without adaptation), outperforming state-of-the-art method
A dual-staged classification-selection approach for automated update of biometric templates
No full textIn the emerging field of adaptive biometrics, systems aim to adapt enrolled templates to variations in samples observed during operations. However, despite numerous advantages, few commercial vendors have adopted auto-update procedures in their products. This is due to limitations associated with existing adaptation schemes. This paper proposes a dual-staged template adaptation scheme that allows to capture `informative' operational samples with significant variations but without increasing the vulnerability to impostor intrusion. This is achieved through a two staged classification-selection approach driven by the harmonic function and risk minimization technique, over a graph based representation of (enrolment and operational) samples. Experimental results on the DIEE fingerprint data set, explicitly collected for evaluating adaptive biometric systems, demonstrate that the proposed scheme results in 67% reduction in error over the baseline system (without adaptation), outperforming state-of-the-art method
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