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Towards Improving the Representational Bias of Process Mining [chapter]

Wil van der Aalst, Joos Buijs, Boudewijn van Dongen
2012 Lecture Notes in Business Information Processing  
Process discovery-discovering a process model from example behavior recorded in an event log-is one of the most challenging tasks in process mining.  ...  These techniques provide new means to discover, monitor, and improve processes in a variety of application domains.  ...  On the Representational Bias of Process Mining In this section we discuss challenges related to process discovery and explain why an appropriate representational bias [3] needs to be selected.  ... 
doi:10.1007/978-3-642-34044-4_3 fatcat:ah7xkjec5fhkfifdb7fnezkl7u

On the Representational Bias in Process Mining

W.M.P. van der Aalst
2011 2011 IEEE 20th International Workshops on Enabling Technologies: Infrastructure for Collaborative Enterprises  
Therefore, we analyze the role of the representational bias in process mining.  ...  The representational bias should not be driven by the desired graphical representation but by the characteristics of the underlying processes and process discovery techniques.  ...  ACKNOWLEDGMENT The author would like to thank the members of the IEEE Task Force on Process Mining (www.win.tue.nl/ieeetfpm/) and all that contributed to the development of ProM (www. processmining.org  ... 
doi:10.1109/wetice.2011.64 dblp:conf/wetice/Aalst11 fatcat:46z7wjt5hjaz5h4w2uk425n2zu

Not All Reviews Are Equal: Towards Addressing Reviewer Biases for Opinion Summarization

Wenyi Tay
2019 Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop  
We further devise an approach for balanced opinion summarization of reviews using our bias-aware opinion representation.  ...  We propose to model reviewer biases from their review texts and rating distributions, and learn a bias-aware opinion representation.  ...  Acknowledgments I thank the anonymous reviewers for their thorough and insightful comments.  ... 
doi:10.18653/v1/p19-2005 dblp:conf/acl/Tay19 fatcat:sxyi4osncja57auvxoxizmug7u

Detection of Underrepresented Biological Sequences Using Class-Conditional Distribution Models [chapter]

Slobodan Vucetic, Dragoljub Pokrajac, Hongbo Xie, Zoran Obradovic
2003 Proceedings of the 2003 SIAM International Conference on Data Mining  
The obtained results demonstrate the promise of the proposed approach for an efficient reduction of sampling bias in biological databases.  ...  To reduce this sampling bias problem a data mining procedure is proposed for detecting underrepresented relevant sequences.  ...  Such sequences are most likely to qualitatively improve the quality of labeled data and reduce the problem of sampling bias.  ... 
doi:10.1137/1.9781611972733.30 dblp:conf/sdm/VuceticPXO03 fatcat:xdetkknnafbulexarogp7pozpi

Learning Unbiased Representations via Mutual Information Backpropagation [article]

Ruggero Ragonesi, Riccardo Volpi, Jacopo Cavazza, Vittorio Murino
2020 arXiv   pre-print
In particular, we face the case where some attributes (bias) of the data, if learned by the model, can severely compromise its generalization properties.  ...  We are interested in learning data-driven representations that can generalize well, even when trained on inherently biased data.  ...  Conclusions We propose a training procedure to learn representations that are not biased towards dataset-specific attributes.  ... 
arXiv:2003.06430v1 fatcat:o2v3jf2jfbebdlkww2ndo5lotm

CrossWalk: Fairness-enhanced Node Representation Learning [article]

Ahmad Khajehnejad, Moein Khajehnejad, Mahmoudreza Babaei, Krishna P. Gummadi, Adrian Weller, Baharan Mirzasoleiman
2022 arXiv   pre-print
CrossWalk pulls nodes that are near groups' peripheries towards their neighbors from other groups in the embedding space, while preserving the necessary structural properties of the graph.  ...  The key idea is to bias random walks to cross group boundaries, by upweighting edges which (1) are closer to the groups' peripheries or (2) connect different groups in the network.  ...  via the CFI.  ... 
arXiv:2105.02725v2 fatcat:y4smzh7cd5eahok6tw5n2n23om

Revisiting the Foundations of Artificial Immune Systems for Data Mining

Alex A. Freitas, Jon Timmis
2007 IEEE Transactions on Evolutionary Computation  
domain: the components of the AIS -such as its representation, affinity function and immune process -should be tailored for the data and the application.  ...  By problem-oriented approach we mean that, in real-world data mining applications, the design of an AIS should take into account the characteristics of the data to be mined together with the application  ...  This paper is a major extension of that preliminary work, and hopefully it represents a significant contribution towards the challenging goal of understanding the inductive biases of AIS.  ... 
doi:10.1109/tevc.2006.884042 fatcat:p3cfjposlbglpenmxxqjrbho7y

Revisiting the Foundations of Artificial Immune Systems: A Problem-Oriented Perspective [chapter]

Alex A. Freitas, Jon Timmis
2003 Lecture Notes in Computer Science  
domain: the components of the AIS -such as its representation, affinity function and immune process -should be tailored for the data and the application.  ...  By problem-oriented approach we mean that, in real-world data mining applications, the design of an AIS should take into account the characteristics of the data to be mined together with the application  ...  This paper is a major extension of that preliminary work, and hopefully it represents a significant contribution towards the challenging goal of understanding the inductive biases of AIS.  ... 
doi:10.1007/978-3-540-45192-1_22 fatcat:q7jo3hkuobekfncov4pa6q7gmu

Page 8177 of Mathematical Reviews Vol. , Issue 2004j [page]

2004 Mathematical Reviews  
Much more recently, SVMs have become the tool of choice for problems arising in data classification and mining.  ...  L. (1-WI-C; Madison, WI) Data mining via support vector machines. (English summary) System modeling and optimization, XX (Trier, 2001), 91-112, IFIP Int. Fed. Inf. Process., 130, Kluwer Acad.  ... 

An Extended Michigan-Style Learning Classifier System for Flexible Supervised Learning, Classification, and Data Mining [chapter]

Ryan J. Urbanowicz, Gediminas Bertasius, Jason H. Moore
2014 Lecture Notes in Computer Science  
Ongoing research in this domain must address the challenges of modeling complex patterns of association, systems biology (i.e. the integration of different data types to achieve a more holistic perspective  ...  ExSTraCS integrates several successful LCS advancements including attribute tracking/feedback, expert knowledge covering (with four built-in attribute weighting algorithms), a flexible and efficient rule representation  ...  In theory the source of EK is up to the user (i.e. classifier population initialization can be biased towards whatever attributes desired).  ... 
doi:10.1007/978-3-319-10762-2_21 fatcat:t2xszn4jujd4pkbo5cvw363nyy

Service Mining: Using Process Mining to Discover, Check, and Improve Service Behavior

Wil van der Aalst
2013 IEEE Transactions on Services Computing  
-Representational Bias: the selected target language for presenting and constructing process mining results.  ...  C5: Improving the Representational Bias Used for Process Discovery A process discovery technique produces a model using a particular language (e.g., BPMN or Petri nets).  ... 
doi:10.1109/tsc.2012.25 fatcat:rjwv6jo6eretfkwnpv3vpc3ehu

Page 472 of Engineering and Mining Journal Vol. 100, Issue 12 [page]

1915 Engineering and Mining Journal  
Carver* Discussion of gold-recovery methods in placer mining has shown but few improvements aside from the operation of dredging.  ...  In other words, the aim is toward enabling the average individual to realize that an ore contains a metal and what the process of reduction implies.  ... 

A Bias-Variance Analysis of a Real World Learning Problem: The CoIL Challenge 2000

Peter van der Putten, Maarten van Someren
2004 Machine Learning  
The CoIL Challenge 2000 data mining competition attracted a wide variety of solutions, both in terms of approaches and performance.  ...  In this article we use the framework of bias-variance decomposition of error to analyze what caused the wide range of prediction performance.  ...  Research in Amsterdam for their support in preparing the competition, the other members of the CoIL competition committee and the reviewers who helped us to significantly improve the paper.  ... 
doi:10.1023/b:mach.0000035476.95130.99 fatcat:n77qlfbxovfzraysrds554mgvy

Process mining: a research agenda

W.M.P. van der Aalst, A.J.M.M. Weijters
2004 Computers in industry (Print)  
In this paper, we try to put the topic of process mining into context, discuss the main issues around process mining, and finally we introduce the papers in this special issue.  ...  Process mining aims at extracting information from event logs to capture the business process as it is being executed.  ...  The disadvantage seems the inductive bias of the mining technique.  ... 
doi:10.1016/j.compind.2003.10.001 fatcat:areplhnmu5cnxcxocifypwqpbi

Timing in multitasking: Memory contamination and time pressure bias

Jungaa Moon, John R. Anderson
2013 Cognitive Psychology  
The effects were captured by incorporating the timing model of Taatgen and van Rijn (2011) into the ACT-R model for the Space Fortress task (Bothell, 2010) .  ...  There can be systematic biases in time estimation when it is performed in complex multitasking situations.  ...  The authors thank Shawn Betts, Dan Bothell, and Darryl Schneider for providing helpful comments on this work. There are two types of mines: red stationary mine and green moving mine.  ... 
doi:10.1016/j.cogpsych.2013.06.001 pmid:23892230 fatcat:nknby2wjsrgvzeenronckyq7mu
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