A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2019; you can also visit the original URL.
The file type is
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  needs to be selected. ...doi:10.1007/978-3-642-34044-4_3 fatcat:ah7xkjec5fhkfifdb7fnezkl7u
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
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
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
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 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
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
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
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. ...
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
-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
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. ...
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
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
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
« Previous Showing results 1 — 15 out of 61,006 results