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Complementarities in learning from data: Insights from general search

INFORMATION ECONOMICS AND POLICY 2023年 65卷

作者： Schaefer, Maximilian Sapi, Geza Inst Mines Telecom Business Sch 9 Rue Charles Fourier F-91000 Evry France European Commiss Chief Economist Team DG COMP B-1210 St Joost Ten Noode Belgium

The ability to make accurate predictions relating to consumer preferences is a key factor of a digital firm's success. Examples include targeted advertisements and, more broadly, business models relying on capturing consumers' attention. The prediction technologies used to learn consumer preferences rely on consumer generated data. Despite the importance of data-driven technologies, there is a lack of knowledge about the precise role that data-scale plays for prediction accuracy. from a policy perspective, a better understanding about the role of data is needed to assess the risks that "big data" might pose for competition. This article highlights potential complementarities between different data dimensions in algorithmic learning. We analyze our hypothesis using search engine data from Yahoo! and provide evidence that more data in the within-user dimension enhances the efficiency of algorithmic learning in the across-user dimension. Our findings suggest that ignoring these complementarities might lead to underestimating scale advantages from data.

关键词： Antitrust Competition learning from data Search engines

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Structure learning Algorithms for Multidimensional Continuous-Time Bayesian Network Classifiers 11

Structure Learning Algorithms for Multidimensional Continuou...

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International Conference on Probabilistic Graphical Models

作者： Villa-Blanco, Carlos Bregoli, Alessandro Bielza, Concha Larranaga, Pedro Stella, Fabio Univ Politecn Madrid Dept Inteligencia Artificial Computat Intelligence Grp Madrid Spain Univ Milano Bicocca Dept Informat Syst & Commun Models & Algorithms Data & Text Min Milan Italy

learning the structure of continuous-time Bayesian networks directly from data has traditionally been performed using score-based structure learning algorithms. Only recently has a constraint-based method been proposed, proving to be more suitable under specific settings, as in modelling systems with variables having more than two states. As a result, studying diverse structure learning algorithms is essential to learn the most appropriate models according to data characteristics and task-related priorities, such as learning speed or accuracy. This article proposes several alternatives of such algorithms for learning multidimensional continuous-time Bayesian network classifiers, introducing for the first time constraint-based and hybrid algorithms for these models. Nevertheless, these contributions also apply to the simpler one-dimensional classification problem for which only score-based solutions exist in the literature. More specifically, the aforementioned constraint-based structure learning algorithm is first adapted to the supervised classification setting. Then, a novel algorithm of this kind, specifically tailored for the multidimensional classification problem, is presented to improve the learning times for the induction of multidimensional classifiers. Finally, a hybrid algorithm is introduced, attempting to combine the strengths of the score- and constraint-based approaches. Experiments with synthetic data are performed not only to validate the capabilities of the proposed algorithms but also to conduct a comparative study of the available structure learning algorithms.

关键词： Continuous-time Bayesian networks structure learning algorithms Bayesian network classifiers multidimensional classification learning from data

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Multidimensional continuous time Bayesian network classifiers

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2021年第12期36卷 7839-7866页

作者： Villa-Blanco, Carlos Larranaga, Pedro Bielza, Concha Univ Politecn Madrid Dept Inteligencia Artificial Computat Intelligence Grp E-28660 Madrid Spain

The multidimensional classification of multivariate time series deals with the assignment of multiple classes to time-ordered data described by a set of feature variables. Although this challenging task has received almost no attention in the literature, it is present in a wide variety of domains, such as medicine, finance or industry. The complexity of this problem lies in two nontrivial tasks, the learning with multivariate time series in continuous time and the simultaneous classification of multiple class variables that may show dependencies between them. These can be addressed with different strategies, but most of them may involve a difficult preprocessing of the data, high space and classification complexity or ignoring useful interclass dependencies. Additionally, no attention has been given to the development of new multidimensional classifiers of time series based on probabilistic graphical models, even though transparent models can facilitate further understanding of the domain. In this paper, a novel probabilistic graphical model is proposed, which is able to classify a discrete multivariate temporal sequence into multiple class variables while modeling their dependencies. This model extends continuous time Bayesian networks to the multidimensional classification problem, which are able to explicitly represent the behavior of time series that evolve over continuous time. Different methods for the learning of the parameters and structure of the model are presented, and numerical experiments on synthetic and real-world data show encouraging results in terms of performance and learning time with respect to independent classifiers, the current alternative approach under the continuous time Bayesian network paradigm.

关键词： Bayesian network classifiers learning from data multidimensional classification multivariate time series probabilistic graphical models

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Constraint-based and hybrid structure learning of multidimensional continuous-time Bayesian network classifiers

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INTERNATIONAL JOURNAL OF APPROXIMATE REASONING 2023年第1期159卷

learning the structure of continuous-time Bayesian networks directly from data has traditionally been performed using score-based structure learning algorithms. Only recently has a constraint-based method been proposed, proving to be more suitable under specific settings, as in modelling systems with variables having more than two states. As a result, studying diverse structure learning algorithms is essential to learn the most appropriate models according to data characteristics and task-related priorities, such as learning speed or accuracy. This article proposes alternative algorithms for learning multidimensional continuous-time Bayesian network classifiers, introducing, for the first time, constraint -based and hybrid algorithms for these models. Nevertheless, these contributions also apply to the simpler one-dimensional classification problem for which only score-based solutions exist in the literature. More specifically, the aforementioned constraint-based structure learning algorithm is first adapted to the supervised classification setting. Then, a novel algorithm of this kind, specifically tailored for the multidimensional classification problem, is presented to improve the learning times for the induction of multidimensional classifiers. Finally, a hybrid algorithm is introduced, attempting to combine the strengths of the score-and constraint-based approaches. Experiments with synthetic and real-world data are performed not only to validate the capabilities of the proposed algorithms but also to conduct a comparative study of the available competitors.& COPY;2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

关键词： Continuous -time Bayesian networks Structure learning algorithms Bayesian network classifiers Multidimensional classification learning from data

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Intrusion Detection System Modeling Based on learning from Network Traffic data

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KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS 2018年第11期12卷 5568-5587页

作者： Midzic, Admir Avdagic, Zikrija Omanovic, Samir Fac Elect Engn Zmaja Bosne Bb Campus Univ Sarajevo Sarajevo 71000 Bosnia & Herceg

This research uses artificial intelligence methods for computer network intrusion detection system modeling. Primary classification is done using self-organized maps (SOM) in two levels, while the secondary classification of ambiguous data is done using Sugeno type Fuzzy Inference System (FIS). FIS is created by using Adaptive Neuro-Fuzzy Inference System (ANFIS). The main challenge for this system was to successfully detect attacks that are either unknown or that are represented by very small percentage of samples in training dataset. Improved algorithm for SOMs in second layer and for the FIS creation is developed for this purpose. Number of clusters in the second SOM layer is optimized by using our improved algorithm to minimize amount of ambiguous data forwarded to FIS. FIS is created using ANFIS that was built on ambiguous training dataset clustered by another SOM (which size is determined dynamically). Proposed hybrid model is created and tested using NSL KDD dataset. For our research, NSL KDD is especially interesting in terms of class distribution (overlapping). Objectives of this research were: to successfully detect intrusions represented in data with small percentage of the total traffic during early detection stages, to successfully deal with overlapping data (separate ambiguous data), to maximize detection rate (DR) and minimize false alarm rate (FAR). Proposed hybrid model with test data achieved acceptable DR value 0.8883 and FAR value 0.2415. The objectives were successfully achieved as it is presented (compared with the similar researches on NSL KDD dataset). Proposed model can be used not only in further research related to this domain, but also in other research areas.

关键词： intrusion detection learning from data clustering classification

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Bringing proportional recovery into proportion: Bayesian modelling of post-stroke motor impairment

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BRAIN 2020年第7期143卷 2189-2206页

作者： Bonkhoff, Anna K. Hope, Thomas Bzdok, Danilo Guggisberg, Adrian G. Hawe, Rachel L. Dukelow, Sean P. Rehme, Anne K. Fink, Gereon R. Grefkes, Christian Bowman, Howard Univ Hosp Cologne Dept Neurol Cologne Germany Res Ctr Juelich Inst Neurosci & Med INM 3 Cognit Neurosci Julich Germany UCL Queen Sq Inst Neurol London England UCL Wellcome Ctr Human Neuroimaging London England Mila Quebec Artificial Intelligence Inst Montreal PQ Canada McGill Univ Fac Med Montreal Neurol Inst Dept Biomed EngnMcConnell Brain Imaging Ctr Montreal PQ Canada Univ Geneva Med Sch Clin Neurosci CH-1202 Geneva Switzerland Univ Calgary Hotchkiss Brain Inst Dept Clin Neurosci Calgary AB Canada Univ Birmingham Sch Psychol Birmingham W Midlands England Univ Kent Sch Comp Canterbury Kent England

Accurate predictions of motor impairment after stroke are of cardinal importance for the patient, clinician, and healthcare system. More than 10 years ago, the proportional recovery rule was introduced by promising that high-fidelity predictions of recovery following stroke were based only on the initially lost motor function, at least for a specific fraction of patients. However, emerging evidence suggests that this recovery rule is subject to various confounds and may apply less universally than previously assumed. Here, we systematically revisited stroke outcome predictions by applying strategies to avoid confounds and fitting hierarchical Bayesian models. We jointly analysed 385 post-stroke trajectories from six separate studies-one of the largest overall datasets of upper limb motor recovery. We addressed confounding ceiling effects by introducing a subset approach and ensured correct model estimation through synthetic data simulations. Subsequently, we used model comparisons to assess the underlying nature of recovery within our empirical recovery data. The first model comparison, relying on the conventional fraction of patients called 'fitters', pointed to a combination of proportional to lost function and constant recovery. 'Proportional to lost' here describes the original notion of proportionality, indicating greater recovery in case of a more severe initial impairment. This combination explained only 32% of the variance in recovery, which is in stark contrast to previous reports of 480%. When instead analysing the complete spectrum of subjects, 'fitters' and 'non-fitters', a combination of proportional to spared function and constant recovery was favoured, implying a more significant improvement in case of more preserved function. Explained variance was at 53%. Therefore, our quantitative findings suggest that motor recovery post-stroke may exhibit some characteristics of proportionality. However, the variance explained was substantially reduced compared

关键词： motor outcome post-stroke proportional recovery Bayesian hierarchical models model comparison learning from data

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Prediction of High Risk of Deviations in Home Care Deliveries 10

Prediction of High Risk of Deviations in Home Care Deliverie...

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10th International Conference on Probabilistic Graphical Models (PGM)

作者： Madsen, Anders L. Olesen, Kristian G. Lovschall, Heidi Lynge Sondberg-Jeppesen, Nicolaj Jensen, Frank Lindblad, Morten Mogensen, Mads Lause Christensen, Trine Soby HUGIN EXPERT AS Aalborg Denmark Aalborg Univ Dept Comp Sci Aalborg Denmark Digitalizat & Welf Technol Municipal Hjorring Hjorring Denmark Dataproces Grp AS Nibe Denmark TREAT Syst ApS Aalborg Denmark Life Sci Innovat North Denmark Aalborg Denmark

This paper presents a real-world application of Bayesian networks to support existing home care quality supervision. In Denmark home care is delivered by municipalities, where the individual citizen is free to select the service provider, private or public. The aim of our work is to support the home care control process by identifying significant deviations automatically, pointing to reasons for a significant deviation and identifying future home care deliveries where there is a high probability of deviation between granted and delivered care to the individual citizen. Home care is granted as packages of time measured in minutes and we define a too high delivery rate as larger than 150%. In the municipality under study in this work (municipality of Hjorring), the supervision of home care delivery is a manual and time consuming process prone to human error. This paper presents the results of efforts to automate parts of the supervision using Bayesian network modelling and data analysis. The results of the pilot study show significant potential in applying Bayesian network modelling and data analysis to this challenge for the benefit of the municipality, the employees and the citizens.

关键词： Bayesian network learning from data real-world application software

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A Software System for Predicting Patient Flow at the Emergency Department of Aalborg University Hospital 10

A Software System for Predicting Patient Flow at the Emergen...

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10th International Conference on Probabilistic Graphical Models (PGM)

作者： Madsen, Anders L. Olesen, Kristian G. Moller, Jorn Munkhof Sondberg-Jeppesen, Nicolaj Jensen, Frank Larsen, Thomas Mulvad Henriksen, Per Lindblad, Morten Christensen, Trine Soby HUGIN EXPERT AS Aalborg Denmark Aalborg Univ Dept Comp Sci Aalborg Denmark Aalborg Univ Hosp Aalborg Denmark Reg Nordjylland Aalborg Denmark Dataproc Grp AS Nibe Denmark Life Sci Innovat North Denmark Aalborg Denmark

This paper presents a software system for predicting patient flow at the emergency department of Aalborg University Hospital. The system uses Bayesian networks as the underlying technology for the predictions. A Bayesian network model has been developed for predicting the hourly rate of patients arriving at the emergency department at Aalborg University Hospital. One advantage of using Bayesian networks is that domain knowledge and historical data can easily be combined into an intuitive graphical model. The aim of this paper is to describe the software system delivering the predictions of the Bayesian network model as a decision-support system for employee shift scheduling at the emergency department.

关键词： Bayesian network learning from data real-world application software

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Stacking-Based Integrated Machine learning with data Reduction 9th

Stacking-Based Integrated Machine Learning with Data Reducti...

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9th KES International Conference on Intelligent Decision Technologies (KES-IDT)

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

ISBN: (纸本)9783319594217;9783319594200

Integrated machine learning is understood as integration of the data reduction with the learning process. Such integration allows to introduce adaptation mechanisms within the learning process by modification of the data with a view to finding its better representation from the point of view of the learning performance criterion. data modification can be carried out through data reduction in both dimensions, i.e. the feature and the instance ones producing the set of prototypes. Currently, data reduction has become a crucial technique for big data analysis and improvement of the machine learning process results. In this paper the stacking technique has been proposed for improving the process of the integrated machine classification and to assure diversification among prototypes. To validate the proposed approach we have carried-out computational experiment. The paper includes the description of the approach and the discussion of the validating experiment results.

关键词： Classification learning from data data reduction Prototype selection Stacked generalization

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Stacking and Rotation-based Technique for Machine learning Classification with data Reduction

Stacking and Rotation-based Technique for Machine Learning C...

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IEEE International Conference on INnovations in Intelligent SysTems and Applications (INISTA)

作者： Czarnowski, Ireneusz Jedrzejowicz, Piotr Gdynia Maritime Univ Dept Informat Syst Morska 83 PL-81225 Gdynia Poland

ISBN: (纸本)9781509057955

The paper focuses on using stacking and rotation-based technique to improve performance and generalization ability of the machine learning classification with data reduction. The aim of data reduction technique is decreasing the quantity of information required to learn a high quality classifiers, especially when the data are huge. The paper shows that merging both stacking and rotation-based ensemble techniques with machine classification based on data reduction may bring additional benefits with respect to the accuracy of the classification process. The finding that has been confirmed by computational experiments. The paper includes the description of the approach and the discussion of the computational experiment results.

关键词： classification learning from data data reduction prototype selection stacked generalization rotation technique

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