1,721,093 research outputs found
Explainable clinical decision support system: opening black-box meta-learner algorithm expert's based
Full text linkMathematical optimization methods are the basic mathematical tools of all artificial intelligence theory. In the field of machine learning and deep learning the examples with which algorithms learn (training data) are used by sophisticated cost functions which can have solutions in closed form or through approximations. The interpretability of the models used and the relative transparency, opposed to the opacity of the black-boxes, is related to how the algorithm learns and this occurs through the optimization and minimization of the errors that the machine makes in the learning process. In particular in the present work is introduced a new method for the determination of the weights in an ensemble model, supervised and unsupervised, based on the well known Analytic Hierarchy Process method (AHP). This method is based on the concept that behind the choice of different and possible algorithms to be used in a machine learning problem, there is an expert who controls the decisionmaking process. The expert assigns a complexity score to each algorithm (based on the concept of complexity-interpretability trade-off) through which the weight with which each model contributes to the training and prediction phase is determined.
In addition, different methods are presented to evaluate the performance of these algorithms and explain how each feature in the model contributes to the prediction of the outputs. The interpretability techniques used in machine learning are also combined with the method introduced based on AHP in the context of clinical decision support systems in order to make the algorithms (black-box) and the results interpretable and explainable, so that clinical-decision-makers can take controlled decisions together with the concept of "right to explanation" introduced by the legislator, because the decision-makers have a civil and legal responsibility of their choices in the clinical field based on systems that make use of artificial intelligence. No less, the central point is the interaction between the expert who controls the algorithm construction process and the domain expert, in this case the clinical one. Three applications on real data are implemented with the methods known in the literature and with those proposed in this work: one application concerns cervical cancer, another the problem related to diabetes and the last one focuses on a specific pathology developed by HIV-infected individuals. All applications are supported by plots, tables and explanations of the results, implemented through Python libraries. The main case study of this thesis regarding HIV-infected individuals concerns an unsupervised ensemble-type problem, in which a series of clustering algorithms are used on a set of features and which in turn produce an output used again as a set of meta-features to provide a set of labels for each given cluster. The meta-features and labels obtained by choosing the best algorithm are used to train a Logistic regression meta-learner, which in turn is used through some explainability methods to provide the value of the contribution that each algorithm has had in the training phase. The use of Logistic regression as a meta-learner classifier is motivated by the fact that it provides appreciable results and also because of the easy explainability of the estimated coefficients
supplementary – Supplemental material for Cerebral blood flow and cerebrovascular reactivity correlate with severity of motor symptoms in Parkinson’s disease
No full textSupplemental material, supplementary for Cerebral blood flow and cerebrovascular reactivity correlate with severity of motor symptoms in Parkinson’s disease by Laura Pelizzari, Maria Marcella Laganà, Federica Rossetto, Niels Bergsland, Mirco Galli, Giuseppe Baselli, Mario Clerici, Raffaello Nemni and Francesca Baglio in Therapeutic Advances in Neurological Disorders</p
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
Variable space transformation techniques and new algorithms for global optimization
Full text linkSolving a global optimization problem is a hard task. In the chapters of this thesis variable space transformation techniques and new algorithmic approaches are proposed to deal with such hard problems. In the first research investigation some variable space transformation techniques are defined as a tool that can be helpfully integrated in (almost) all algorithm frameworks. In particular the focus will be on piecewise linear and non-linear transformations that allow to tackle the problem advantageously. After introducing the theory, preliminary numerical experiments are reported exploiting the transformations in a simple multi-start framework. The idea is to gather the information obtained during a multi-start approach and to apply a sequence of transformations in the variable space that makes the exploration easier. The aim is to expand the attraction basins of global minimizers shrinking those of the local minima already found. Preliminary considerations are made about the possibility to use these transformations as derivative-free preconditioners. The second research investigation concerns the definition of an efficient algorithm on a wide spectrum of global optimization problems. In particular, will be discussed how to do an accurate exploratory geometry and a space search reduction strategy, recently renamed in literature as zoom-in strategy, in a probabilistic algorithm that can speed up significantly the convergence towards better solutions. After introducing the algorithm framework named GABRLS, presented as the winner of the generalization-based Contest in Global Optimization (GENOPT 2017, [61]), the approach is extended to handle also non-continuous variables. The resulting algorithm has been tested in a real case study of design optimization of electric motor. The case study provides evidences about the promising exploratory geometry of the approach in quickly finding feasible and optimal solutions to a mixed-integer constrained problem. In the last research investigation, a new black-box approach is proposed to tackle a real case study of the spare part management problem of a fleet of aircraft. In particular, for this specific type of inventory problem, a black-box model and a tailored global optimization algorithm is defined. The aim is to address the non-linearity of the problem as is, without any decomposition in sub-problem and without any approximation or necessity to check ex post the feasibility of the solution. The main contribution consists of advancing the existing literature for multi-item inventory systems through an enhanced time-effective optimization algorithm tested in a single-echelon system
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
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