1,721,070 research outputs found
The effect of class distribution on classifier learning: an empirical study
No full textIn this article we analyze the effect of class distribution on classifier learning. We begin by describing the different ways in which class distribution affects learning and how it affects the evaluation of learned classifiers. We then present the results of two comprehensive experimental studies. The first study compares the performance of classifiers generated from unbalanced data sets with the performance of classifiers generated from balanced versions of the same data sets. This comparison allows us to isolate and quantify the effect that the training set's class distribution has on learning and contrast the performance of the classifiers on the minority and majority classes. The second study assesses what distribution is "best" for training, with respect to two performance measures: classification accuracy and the area under the ROC curve (AUC). A tacit assumption behind much research on classifier induction is that the class distribution of the training data should match the "natural" distribution of the data. This study shows that the naturally occurring class distribution often is not best for learning, and often substantially better performance can be obtained by using a different class distribution. Understanding how classifier performance is affected by class distribution can help practitioners to choose training data--in real-world situations the number of training examples often must be limited due to computational costs or the costs associated with procuring and preparing the data.Technical report ML-TR-4
The effect of class distribution on classifier learning
No full textMany of today's large data sets must be reduced in size before invoking inductive algorithms, due to the costs associated with procuring/processing the data, and because most of these algorithms cannot handle enormous amounts of data. In these cases it is important to select the training data carefully so the impact on classifier performance is minimized. A tacit assumption behind much research on classifier induction is that the class distribution of the training data should match the "natural" distribution of the data. In this paper we analyze the relationship between training class distribution and classifier performance on 25 data sets and show that the natural distribution usually is not the best distribution for learning--a different class distribution should generally be chosen when the dataset size must be limited. We also explain how changing the class distribution of the training set affects classifier learning and why one training distribution might be better than another.Technical report ml-tr-4
The Myth of the Double-Blind Review? Author Identification Using Only Citations
Get PDFPrior studies have questioned the degree of anonymity of the double-blind review process for scholarly research articles. For example, one study based on a survey of reviewers concluded that authors often could be identified by reviewers using a combination of the author's reference list and the referee's personal background knowledge. For the KDD Cup 2003 competition's "Open Task" we examined how well various automatic matching techniques could identify authors within the competition's very large archive of research papers. This paper describes the issues surrounding author identification, how these issues motivated our study, and the results we obtained. The best method, based on discriminative self-citations, identified authors correctly 40-45% of the time. One main motivation for double-blind review is to eliminate bias in favor of well-known authors. However, identification accuracy for authors with substantial publication history is even better (60% accuracy for the top-10% most prolific authors, 85% for authors with 100 or more prior papers).NYU, Stern School of Business, IOMS Department, Center for Digital Economy Researc
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
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
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
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