2,575 research outputs found

    An investigation into machine learning approaches for forecasting spatio-temporal demand in ride-hailing service

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    In this paper, we present machine learning approaches for characterizing and forecasting the short-term demand for on-demand ride-hailing services. We propose the spatio-temporal estimation of the demand that is a function of variable effects related to traffic, pricing and weather conditions. With respect to the methodology, a single decision tree, bootstrap-aggregated (bagged) decision trees, random forest, boosted decision trees, and artificial neural network for regression have been adapted and systematically compared using various statistics, e.g. R-square, Root Mean Square Error (RMSE), and slope. To better assess the quality of the models, they have been tested on a real case study using the data of DiDi Chuxing, the main on-demand ride hailing service provider in China. In the current study, 199,584 time-slots describing the spatio-temporal ride-hailing demand has been extracted with an aggregated-time interval of 10 mins. All the methods are trained and validated on the basis of two independent samples from this dataset. The results revealed that boosted decision trees provide the best prediction accuracy (RMSE=16.41), while avoiding the risk of over-fitting, followed by artificial neural network (20.09), random forest (23.50), bagged decision trees (24.29) and single decision tree (33.55).Comment: Currently under review for journal publicatio

    Bayesian Networks with Expert Elicitation as Applicable to Student Retention in Institutional Research

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    The application of Bayesian networks within the field of institutional research is explored through the development of a Bayesian network used to predict first- to second-year retention of undergraduates. A hybrid approach to model development is employed, in which formal elicitation of subject-matter expertise is combined with machine learning in designing model structure and specification of model parameters. Subject-matter experts include two academic advisors at a small, private liberal arts college in the southeast, and the data used in machine learning include six years of historical student-related information (i.e., demographic, admissions, academic, and financial) on 1,438 first-year students. Netica 5.12, a software package designed for constructing Bayesian networks, is used for building and validating the model. Evaluation of the resulting model’s predictive capabilities is examined, as well as analyses of sensitivity, internal validity, and model complexity. Additionally, the utility of using Bayesian networks within institutional research and higher education is discussed. The importance of comprehensive evaluation is highlighted, due to the study’s inclusion of an unbalanced data set. Best practices and experiences with expert elicitation are also noted, including recommendations for use of formal elicitation frameworks and careful consideration of operating definitions. Academic preparation and financial need risk profile are identified as key variables related to retention, and the need for enhanced data collection surrounding such variables is also revealed. For example, the experts emphasize study skills as an important predictor of retention while noting the absence of collection of quantitative data related to measuring students’ study skills. Finally, the importance and value of the model development process is stressed, as stakeholders are required to articulate, define, discuss, and evaluate model components, assumptions, and results

    AI alignment and generalization in deep learning

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    This thesis covers a number of works in deep learning aimed at understanding and improving generalization abilities of deep neural networks (DNNs). DNNs achieve unrivaled performance in a growing range of tasks and domains, yet their behavior during learning and deployment remains poorly understood. They can also be surprisingly brittle: in-distribution generalization can be a poor predictor of behavior or performance under distributional shifts, which typically cannot be avoided in practice. While these limitations are not unique to DNNs -- and indeed are likely to be challenges facing any AI systems of sufficient complexity -- the prevalence and power of DNNs makes them particularly worthy of study. I frame these challenges within the broader context of "AI Alignment": a nascent field focused on ensuring that AI systems behave in accordance with their user's intentions. While making AI systems more intelligent or capable can help make them more aligned, it is neither necessary nor sufficient for alignment. However, being able to align state-of-the-art AI systems (e.g. DNNs) is of great social importance in order to avoid undesirable and unsafe behavior from advanced AI systems. Without progress in AI Alignment, advanced AI systems might pursue objectives at odds with human survival, posing an existential risk (``x-risk'') to humanity. A core tenet of this thesis is that the achieving high performance on machine learning benchmarks if often a good indicator of AI systems' capabilities, but not their alignment. This is because AI systems often achieve high performance in unexpected ways that reveal the limitations of our performance metrics, and more generally, our techniques for specifying our intentions. Learning about human intentions using DNNs shows some promise, but DNNs are still prone to learning to solve tasks using concepts of "features" very different from those which are salient to humans. Indeed, this is a major source of their poor generalization on out-of-distribution data. By better understanding the successes and failures of DNN generalization and current methods of specifying our intentions, we aim to make progress towards deep-learning based AI systems that are able to understand users' intentions and act accordingly.Cette thèse discute quelques travaux en apprentissage profond visant à comprendre et à améliorer les capacités de généralisation des réseaux de neurones profonds (DNN). Les DNNs atteignent des performances inégalées dans un éventail croissant de tâches et de domaines, mais leur comportement pendant l'apprentissage et le déploiement reste mal compris. Ils peuvent également être étonnamment fragiles: la généralisation dans la distribution peut être un mauvais prédicteur du comportement ou de la performance lors de changements de distribution, ce qui ne peut généralement pas être évité dans la pratique. Bien que ces limitations ne soient pas propres aux DNN - et sont en effet susceptibles de constituer des défis pour tout système d'IA suffisamment complexe - la prévalence et la puissance des DNN les rendent particulièrement dignes d'étude. J'encadre ces défis dans le contexte plus large de «l'alignement de l'IA»: un domaine naissant axé sur la garantie que les systèmes d'IA se comportent conformément aux intentions de leurs utilisateurs. Bien que rendre les systèmes d'IA plus intelligents ou capables puisse aider à les rendre plus alignés, cela n'est ni nécessaire ni suffisant pour l'alignement. Cependant, être capable d'aligner les systèmes d'IA de pointe (par exemple les DNN) est d'une grande importance sociale afin d'éviter les comportements indésirables et dangereux des systèmes d'IA avancés. Sans progrès dans l'alignement de l'IA, les systèmes d'IA avancés pourraient poursuivre des objectifs contraires à la survie humaine, posant un risque existentiel («x-risque») pour l'humanité. L'un des principes fondamentaux de cette thèse est que l'obtention de hautes performances sur les repères d'apprentissage automatique est souvent un bon indicateur des capacités des systèmes d'IA, mais pas de leur alignement. En effet, les systèmes d'IA atteignent souvent des performances élevées de manière inattendue, ce qui révèle les limites de nos mesures de performance et, plus généralement, de nos techniques pour spécifier nos intentions. L'apprentissage des intentions humaines à l'aide des DNN est quelque peu prometteur, mais les DNN sont toujours enclins à apprendre à résoudre des tâches en utilisant des concepts de «caractéristiques» très différents de ceux qui sont saillants pour les humains. En effet, c'est une source majeure de leur mauvaise généralisation sur les données hors distribution. En comprenant mieux les succès et les échecs de la généralisation DNN et les méthodes actuelles de spécification de nos intentions, nous visons à progresser vers des systèmes d'IA basés sur l'apprentissage en profondeur qui sont capables de comprendre les intentions des utilisateurs et d'agir en conséquence

    A Comprehensive Survey on Deep Learning Techniques in Educational Data Mining

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    Educational Data Mining (EDM) has emerged as a vital field of research, which harnesses the power of computational techniques to analyze educational data. With the increasing complexity and diversity of educational data, Deep Learning techniques have shown significant advantages in addressing the challenges associated with analyzing and modeling this data. This survey aims to systematically review the state-of-the-art in EDM with Deep Learning. We begin by providing a brief introduction to EDM and Deep Learning, highlighting their relevance in the context of modern education. Next, we present a detailed review of Deep Learning techniques applied in four typical educational scenarios, including knowledge tracing, undesirable student detecting, performance prediction, and personalized recommendation. Furthermore, a comprehensive overview of public datasets and processing tools for EDM is provided. Finally, we point out emerging trends and future directions in this research area.Comment: 21 pages, 5 figure

    A SLR on Customer Dropout Prediction

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    Dropout prediction is a problem that is being addressed with machine learning algorithms; thus, appropriate approaches to address the dropout rate are needed. The selection of an algorithm to predict the dropout rate is only one problem to be addressed. Other aspects should also be considered, such as which features should be selected and how to measure accuracy while considering whether the features are appropriate according to the business context in which they are employed. To solve these questions, the goal of this paper is to develop a systematic literature review to evaluate the development of existing studies and to predict the dropout rate in contractual settings using machine learning to identify current trends and research opportunities. The results of this study identify trends in the use of machine learning algorithms in different business areas and in the adoption of machine learning algorithms, including which metrics are being adopted and what features are being applied. Finally, some research opportunities and gaps that could be explored in future research are presented.info:eu-repo/semantics/publishedVersio
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