1,721,043 research outputs found
F-Measure Curves for Visualizing Classifier Performance with Imbalanced Data
No full textTraining classifiers using imbalanced data is a challenging problem in many real-world recognition applications due in part to the bias in performance that occur for: (1) classifiers that are often optimized and compared using unsuitable performance measurements for imbalance problems; (2) classifiers that are trained and tested on a fixed imbalance level of data, which may differ from operational scenarios; (3) cases where the preference of correct classification of classes is application dependent. Specialized performance evaluation metrics and tools are needed for problems that involve class imbalance, 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 propose a global evaluation space for the scalar F-measure metric 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 imbalance levels for the desired preference of true positive rate to precision. Experiments with synthetic data show the benefits of evaluating and comparing classifiers under different operating conditions in the proposed F-measure space over ROC, precision-recall, and cost spaces
Loss factors for learning Boosting ensembles from imbalanced data
No full textClass imbalance is an issue in many real world applications because classification algorithms tend to misclassify instances from the class of interest when its training samples are outnumbered by those of other classes. Several variations of AdaBoost ensemble method have been proposed in literature to learn from imbalanced data based on re-sampling. However, their loss factor is based on standard accuracy, which still biases performance towards the majority class. This problem is mitigated using cost-sensitive Boosting algorithms, although it can be avoided at the outset by modifying the loss factor calculation. In this paper, two loss factors, based on F-measure and G-mean are proposed that are more suitable to deal with imbalanced data during the Boosting learning process. The performance of standard AdaBoost and of three specialized versions for class imbalance (SMOTEBoost, RUSBoost, and RB-Boost) are empirically evaluated using the proposed loss factors, both on synthetic data and on a real-world face re-identification task. Experimental results show a significant performance improvement on AdaBoost and RUSBoost with the proposed loss factors
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe 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
Get PDF“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
Get PDFWe 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
Why template self-update should work in biometric authentication systems?
No full textThe term adaptive biometric systems refers to biometric recognition systems in which an algorithm aimed to follow variations of the clients appearance has been implemented. Among others, the self update algorithm is used when only one biometric is available, and is able to add to the clients gallery novel data collected during system operation, on the basis of a updating threshold: if the novel data, compared with existing template(s), provide a matching score higher than the given threshold, they are added to the gallery. In order to avoid misclassification errors, thus inserting impostors into the clients gallery, this threshold is very conservative. Self-update algorithm has shown to be effective for many biometrics. However, no work tried to explain, so far, why self-update should work, in particular when a very conservative update threshold is used (zeroFAR threshold). This is the goal of the present paper, which provides a conceptual explanation of the self update mechanism coupled with a set of experiments on a publicly available data set explicitly designed for studying adaptive biometric systems
Classifier Ensembles with Trajectory Under-Sampling for Face Re-Identification
Get PDFClass imbalance is an issue in many real world applications because classification algorithms tend to misclassify instances from the class of interest when its training samples are outnumbered by those of other classes. Several variations of AdaBoost ensemble method have been proposed in literature to learn from imbalanced data based on re-sampling. However, their loss factor is based on standard accuracy, which still biases performance towards the majority class. This problem is mitigated using cost-sensitive Boosting algorithms, although it can be avoided at the outset by modifying the loss factor calculation. In this paper, two loss factors, based on F-measure and G-mean are proposed that are more suitable to deal with imbalanced data during the Boosting learning process. The performance of standard AdaBoost and of three specialized versions for class imbalance (SMOTEBoost, RUSBoost, and RB-Boost) are empirically evaluated using the proposed loss factors, both on synthetic data and on a real-world face re-identification task. Experimental results show a significant performance improvement on AdaBoost and RUSBoost with the proposed loss factors
An extended sparse classification framework for domain adaptation in video surveillance
Get PDFStill-to-video face recognition (FR) systems used in video surveillance applications capture facial trajectories across a network of distributed video cameras and compare them against stored distributed facial models. Currently, the performance of state-of-the-art systems is severely affected by changes in facial appearance caused by variations in, e.g., pose, illumination and scale in different camera viewpoints. Moreover, since an individual is typically enrolled using one or few reference stills captured during enrolment, face models are not robust to intra-class variation. In this paper, the Extended Sparse Representation Classification through Domain Adaptation (ESRC-DA) algorithm is proposed to improve performance of still-to-video FR. The systemâs facial models are thereby enhanced by integrating variational information from its operational domain. In particular, robustness to intra-class variations is improved by exploiting: (1) an under-sampled dictionary from target reference facial stills captured under controlled conditions; and (2) an auxiliary dictionary from an abundance of unlabelled facial trajectories captured under different conditions, from each camera viewpoint in the surveillance network. Accuracy and efficiency of the proposed technique is compared to state-of-the-art still-to-video FR techniques using videos from the Chokepoint and COX-S2V databases. Results indicate that ESRC-DA with dictionary learning of unlabelled trajectories provides the highest level of accuracy, while maintaining a low complexity
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe 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
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