1,720,968 research outputs found
Improving multi-label classification using inter-label associations and a new Kalman filter based ensemble method
Get PDFIn machine learning, classification algorithms are used to train models to recognise the class, or category, that an object belongs to. Most classification problems are multi-class, in which one object can belong to at most one class. However, there are many important real-world problems in which an object can belong to more than one class simultaneously. These are known as multi-label classification problems as an object can be labelled with more than one class. The multi-label classification algorithms in the literature range from very simple approaches, such as binary relevance, in which independent binary classifiers are built for each label, to sophisticated ensemble techniques, such as classifier chains, that build collections of interconnected classifiers. The most effective approaches tend to explicitly exploit relationships between the labels themselves, inter-label associations, and use ensembles. There is an opportunity, however, to more explicitly take advantage of inter-label associations and to use ensembling techniques that are more sophisticated than the bagging-based approaches that dominate the multi-label classification literature. There are several multi-label classification algorithms in the literature. The most basic methods are binary relevance and label powerset. Binary relevance considers each label as independent binary classification task and learns binary classifier models. Label powerset converts unique label assignment combinations to unique classes and then trains a multi-class classifier model. Although there are other methods which can benefit by considering the inter-label associations or through ensemble algorithms. Ensemble methods in multi-class domain generally perform much better than the individual classifier models. Although, except bagging like methods, there are not much work done in multi-label on boosting or boosting-like methods. This thesis investigates new algorithms for training multi-label classification models that exploit inter-label associations, and/or utilise ensemble models (especially boosting-like methods). Three new methods are proposed: Stacked-MLkNN, a stacked-ensemble-based lazy learning algorithm that exploits inter-label associations at the stacked layer; CascadeML, a neural network training algorithm that uses a cascade architecture to exploit inter-label associations and evolves the network architecture during training which minimises the requirement for hyperparameter tuning; and KFHE-HOMER, a multi-label ensemble training algorithm built using a newly proposed perspective on ensemble training that views it as a static state estimation problem that can be solved using the sensor fusion properties of the Kalman filter. This new perspective on ensemble training is also a contribution of this thesis, as are two new multi-class classification algorithms--- Kalman Filter-based Heuristic Ensemble (KFHE) and KalmanTune---that exploit it. Each newly proposed method is extensively evaluated across a set of well-known benchmark multi-label classification datasets, and compared to the performance of current state-of-the art methods. Each newly proposed method is found to be highly effective. Stacked-MLkNN performs better than all other existing instance-based multi-label classification algorithms against which it was compared. CascadeML can create models with comparable performance to the best performing multi-label methods, without requiring extensive hyperparameter tuning. KFHE outperforms leading multi-class ensemble methods, and KalmanTune can improve the performance of ensembles trained using boosting. Finally, KFHE-HOMER was found to perform better than all other multi-label classification methods against which it was compared
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
Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters
Get PDFThis paper introduces a new perspective on multi-class ensemble classification that considers training an ensemble as a state estimation problem. The new perspective considers the final ensemble classifier model as a static state, which can be estimated using a Kalman filter that combines noisy estimates made by individual classifier models. A new algorithm based on this perspective, the Kalman Filter-based Heuristic Ensemble (KFHE), is also presented in this paper which shows the practical applicability of the new perspective. Experiments performed on 30 datasets compare KFHE with state-of-the-art multi-class ensemble classification algorithms and show the potential and effectiveness of the new perspective and algorithm. Existing ensemble approaches trade off classification accuracy against robustness to class label noise, but KFHE is shown to be significantly better or at least as good as the state-of-the-art algorithms for datasets both with and without class label noise.Science Foundation IrelandInsight Research Centr
Author-wise bibliometric analysis based on entropy.
No full textAuthor-wise bibliometric analysis based on entropy.</p
- …
