1,720,958 research outputs found
SurvivalLVQ: Interpretable supervised clustering and prediction in survival analysis via Learning Vector Quantization
No full textThe authors acknowledge the support from the Flemish Government (through the AI Research Program) and the Research Fund Flanders (project G0A2120N)
BELLATREX: Building Explanations Through a LocaLly AccuraTe Rule EXtractor
Get PDFsponsorship: This work was supported in part by the Flemish Government (AI Research Program), and in part by the Research Fund Flanders under Project G080118N and Project G0A2120N. (Flemish Government (AI Research Program), Research Fund Flanders|G080118N, Research Fund Flanders|G0A2120N)status: Publishe
Explaining a Random Survival Forest by Extracting a few prototype rules
No full textsponsorship: This research received funding from the Flemish Government (AI Research Program) and the Research Fund Flanders (project G080118N). (Flemish Government (AI Research Program), Research Fund Flanders|G080118N)status: Publishe
Explanatory Techniques for Machine Learning Models
No full textSince the last decade, we are assisting a widespread use of “black box” Machine Learning algorithms, these are algorithms with excellent performance but whose outcomes are hard to understand to a human agent. However, there are some situation when it is important to understand why a certain output is given,and the field of explanability in Machine Learning has flourished in the last decade. In this work, we will go through some of these techniques. We will focus on model agnostic visualisation techniques introduced by Friedman (2001) and developed by Goldstein et al. (2015). Starting from the Partial Dependence plotting technique, we then analyse the Individual Conditional Expectation plot and its variants. Among them, we suggest the introduction of the so called “d-log-ICE” and we try identify scenarios where this techniques can bring better interpretability. We test our techniques on two models, the first one is based on the Boston Housing Dataset, and the second is an internal model at ABN Amro called “FLAG”.Machine Learnin
Comparing the prediction performance of item response theory and machine learning methods on item responses for educational assessments
Get PDFTo obtain more accurate and robust feedback information from the students' assessment outcomes and to communicate it to students and optimize teaching and learning strategies, educational researchers and practitioners must critically reflect on whether the existing methods of data analytics are capable of retrieving the information provided in the database. This study compared and contrasted the prediction performance of an item response theory method, particularly the use of an explanatory item response model (EIRM), and six supervised machine learning (ML) methods for predicting students' item responses in educational assessments, considering student- and item-related background information. Each of seven prediction methods was evaluated through cross-validation approaches under three prediction scenarios: (a) unrealized responses of new students to existing items, (b) unrealized responses of existing students to new items, and (c) missing responses of existing students to existing items. The results of a simulation study and two real-life assessment data examples showed that employing student- and item-related background information in addition to the item response data substantially increases the prediction accuracy for new students or items. We also found that the EIRM is as competitive as the best performing ML methods in predicting the student performance outcomes for the educational assessment datasets.sponsorship: This work was carried out within imec's Smart Education research programme, with support from the Flemish government. This research received funding from the Flemish AI Research Program. Also, this work was supported by the 2021 Research Fund of the University of Seoul for Jinho Kim. (Flemish government, Flemish AI Research Program, University of Seoul)status: Publishe
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
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