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Machine Learning in Business Process Monitoring: A Comparison of Deep Learning and Classical Approaches Used for Outcome Prediction

Wolfgang Kratsch, Jonas Manderscheid, Maximilian Röglinger, Johannes Seyfried
2020 Business & Information Systems Engineering  
Although deep learning (DL) has yielded breakthroughs, most existing approaches build on classical machine learning (ML) techniques, particularly when it comes to outcome-oriented predictive process monitoring  ...  Third, logs with a high activity-to-instance payload ratio (i.e., input data is predominantly generated at runtime) call for the application of long short term memory networks.  ...  as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.  ... 
doi:10.1007/s12599-020-00645-0 fatcat:4swzksz6g5hibhjg2svohhxybm

A machine learning approach to portfolio pricing and risk management for high-dimensional problems [article]

Lucio Fernandez-Arjona
2022 arXiv   pre-print
We present a general framework for portfolio risk management in discrete time, based on a replicating martingale. This martingale is learned from a finite sample in a supervised setting.  ...  The model learns the features necessary for an effective low-dimensional representation, overcoming the curse of dimensionality common to function approximation in high-dimensional spaces.  ...  The third and final alternative is to use a neural network that matches the number of basis functions m. This means, for both T = 5 and T = 40, that p = 286.  ... 
arXiv:2004.14149v4 fatcat:qstkax3rjzbrdecfvr2rmx7t7u

Predicting Fiscal Crises: A Machine Learning Approach

Klaus-Peter Hellwig
2021 IMF Working Papers  
The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.  ...  Therefore, in addition to exploring novel algorithms and data imputation, the paper emphasizes the need for a machine learning approach to model evaluation: throughout the paper, the focus is on out-of-sample  ...  It then compares the econometric approach with machine learning algorithms before discussing imputation and sample pooling. A.  ... 
doi:10.5089/9781513573588.001 fatcat:ea762i2ja5bt7k6uyk47ge3c2m

Greenfield FDI attractiveness index: a machine learning approach

Ilan Alon, Vanessa P.G. Bretas, Alex Sclip, Andrea Paltrinieri
2022 Competitiveness Review: an international business journal  
Purpose This study aims to propose a comprehensive greenfield foreign direct investment (FDI) attractiveness index using exploratory factor analysis and automated machine learning (AML).  ...  This study's index is developed in a robust three-stage process.  ...  Machine learning approach 3.2.2 Automated machine learning.  ... 
doi:10.1108/cr-12-2021-0171 fatcat:fmkranhog5baja6lxwptdv3rx4

Machine Learning for Subtyping Concussion Using a Clustering Approach

Cirelle K. Rosenblatt, Alexandra Harriss, Aliya-Nur Babul, Samuel A. Rosenblatt
2021 Frontiers in Human Neuroscience  
of interdisciplinary expertise.Objective: The purpose of this study was to determine whether a bottom-up, unsupervised, machine learning approach, could more accurately support concussion subtyping.Methods  ...  Concussion subtypes derived demonstrated clinically distinct profiles, with statistically significant differences (p < 0.05) between all five clusters.Conclusion: This machine learning approach enabled  ...  Hierarchical agglomerative clustering, is a unsupervised, bottom-up, machine learning approach that can identify subgroups from complex data and provide an opportunity to classify clinical patterns as  ... 
doi:10.3389/fnhum.2021.716643 pmid:34658816 pmcid:PMC8514654 fatcat:gel2ldmemfar7gluktwp4a3moi

Extending the Machine Learning Abstraction Boundary: A Complex Systems Approach to Incorporate Societal Context [article]

Donald Martin Jr., Vinodkumar Prabhakaran, Jill Kuhlberg, Andrew Smart, William S. Isaac
2020 arXiv   pre-print
Finally, we identify community based system dynamics (CBSD) as a powerful, transparent and rigorous approach for practicing CCTF during all phases of the ML product development process.  ...  Machine learning (ML) fairness research tends to focus primarily on mathematically-based interventions on often opaque algorithms or models and/or their immediate inputs and outputs.  ...  In large part this revolution has been driven by recent advancements, such as deep learning, in machine learning model design and development.  ... 
arXiv:2006.09663v1 fatcat:oelh2r2m7nbqldypsb4bg6p6qe

Understanding food inflation in India: A Machine Learning approach [article]

Akash Malhotra, Mayank Maloo
2017 arXiv   pre-print
The relative significance of these factors in determining the change in food prices have been analysed using gradient boosted regression trees (BRT), a machine learning technique.  ...  The primary reason behind this stubborn food inflation is mismatch in supply-demand, as domestic agricultural production has failed to keep up with rising demand owing to a number of proximate factors.  ...  Figure 3 . 3 Cereal PDS supply management (Data Source: DBIE, RBI) Fig. 5 ) 5 .  ... 
arXiv:1701.08789v1 fatcat:366kq6kyrfdg3ntx35rvcv5754

Linking Human And Machine Behavior: A New Approach to Evaluate Training Data Quality for Beneficial Machine Learning

Thilo Hagendorff
2021 Minds and Machines  
from the big data rationale n = all to a more selective way of processing data for training sets in machine learning.  ...  This is the first study to fill this gap by describing new dimensions of data quality for supervised machine learning applications.  ...  Acknowledgements This research was supported by the Cluster of Excellence "Machine Learning -New Perspectives for Science" funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation  ... 
doi:10.1007/s11023-021-09573-8 pmid:34602749 pmcid:PMC8475847 fatcat:evdtl3raqbhnrfc352merfncj4

Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms

Samuel O. Danso, Zhanhang Zeng, Graciela Muniz-Terrera, Craig W. Ritchie
2021 Frontiers in Big Data  
Risk prediction models for AD based on various computational approaches, including machine learning, are being developed with promising results.  ...  We propose a framework that employs a transfer-learning paradigm with ensemble learning algorithms to develop explainable personalised risk prediction models for dementia.  ...  ACKNOWLEDGMENTS This paper used data from SHARE Waves 1, 2, 3, 4, 5, 6, and  ... 
doi:10.3389/fdata.2021.613047 pmid:34124650 pmcid:PMC8187875 fatcat:7wojqvz355hunig7kiymzzdtxu

Ranking by inspiration: a network science approach

Livio Bioglio, Valentina Rho, Ruggero G. Pensa
2019 Machine Learning  
In bibliographic networks, for instance, an information diffusion process takes place when some authors, that publish papers in a given topic, influence some of their neighbors (coauthors, citing authors  ...  Contagion processes have been widely studied in epidemiology and life science in general, but their implications are largely tangible in other research areas, such as in network science and computational  ...  This work has been partially funded by Project MIMOSA (MultIModal Ontology-driven query system for the heterogeneous data of a SmArtcity, "Progetto di Ateneo Torino_call2014_L2_157", 2015-17).  ... 
doi:10.1007/s10994-019-05828-9 fatcat:ihpsmo3hdfh5phl63cxjsjr5ku

A Machine Learning Approach to Forecast Economic Recessions—An Italian Case Study

Giovanni Cicceri, Giuseppe Inserra, Michele Limosani
2020 Mathematics  
In this paper, the goal is to give a different, although complementary, approach concerning the classical econometric techniques, and to show how Machine Learning (ML) techniques may improve short-term  ...  As a case study, we use Italian data on GDP and a few related variables. In particular, we evaluate the goodness of fit of the forecasting proposed model in a case study of the Italian GDP.  ...  Paul Ormerod for his valuable suggestions in applying Machine Learning approaches to the analysis of GDP growth and Prof.  ... 
doi:10.3390/math8020241 fatcat:p6cwivvfkvcwlgr2eqc3leb26a

A Machine-Learning-Driven Evolutionary Approach for Testing Web Application Firewalls

Dennis Appelt, Cu D. Nguyen, Annibale Panichella, Lionel C. Briand
2018 IEEE Transactions on Reliability  
We present ML-Driven, an approach based on machine learning and an evolutionary algorithm to automatically detect holes in WAFs that let SQL injection attacks bypass them.  ...  Machine learning is used to incrementally learn attack patterns from previously generated attacks according to their testing results, i.e., if they are blocked or bypass the WAF.  ...  In this section, we introduce a machine learning-based approach, called ML-Driven, to sample the input space in a more efficient manner than RAN does.  ... 
doi:10.1109/tr.2018.2805763 fatcat:5iua3xo3rffxxpxfaeaf7pccae

A reinforcement learning approach to autonomous decision-making in smart electricity markets

Markus Peters, Wolfgang Ketter, Maytal Saar-Tsechansky, John Collins
2013 Machine Learning  
Our brokers use Reinforcement Learning with function approximation, they can accommodate arbitrary economic signals from their environments, and they learn efficiently over the large state spaces resulting  ...  We propose a novel class of autonomous broker agents for retail electricity trading that can operate in a wide range of Smart Electricity Markets, and that are capable of deriving long-term, profit-maximizing  ...  Acknowledgements We would like to thank three anonymous Machine Learning reviewers and three anonymous ECML-PKDD 2012 reviewers for their insightful comments on this work.  ... 
doi:10.1007/s10994-013-5340-0 fatcat:hkuew2lwf5eh5lqu5ssqhxtv3a

Creating a robot localization monitor using particle filter and machine learning approaches

Matthias Eder, Michael Reip, Gerald Steinbauer
2021 Applied intelligence (Boston)  
In this paper we present an approach that allows a robot to asses if the localization is still correct. The approach assumes that the underlying localization approach is based on a particle filter.  ...  Because of uncertainties in acting and sensing, and environmental factors such as people flocking around robots, there is always the risk that a robot loses its localization.  ...  Adaptive boosting Adaptive Boosting (AdaBoost) is a machine learning approach which uses supervised data for classification.  ... 
doi:10.1007/s10489-020-02157-6 fatcat:jlk4vfhrp5ah3gggaw4kz6ljeq

The Effects of Geopolitical Uncertainty in Forecasting Financial Markets: A Machine Learning Approach

Vasilios Plakandaras, Periklis Gogas, Theophilos Papadimitriou
2018 Algorithms  
An important ingredient in economic policy planning both in the public or the private sector is risk management.  ...  In doing so, we build forecasting models that are based on machine learning techniques and evaluate the associated out-of-sample forecasting error in various horizons from one to twenty-four months ahead  ...  Unlike other machine learning approaches, the SVR is based on a convex minimization problem with a unique global minimum, avoiding local minima.  ... 
doi:10.3390/a12010001 fatcat:blddeq6iqfh4zhxt6kw4phejfa
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