Publications

Commentaries

Žliobaitė, I. and Fortelius, M. (2016). Peer review: revise rules on conflict of interest. Nature 539(7628), DOI  PDFpublished  PDFlongversion


Žliobaitė, I. and Stenseth, N. Chr. (2016). Improving Adaptation through Evolution and Learning: A Response to Watson and Szathmáry. Trends in Ecology and Evolution 31(12), p. 892-893. PDFpreprint  DOI


Journal Publications


Teittinen, J., Hiienkari, M., Žliobaitė, I. Hollmen, J., Berg, H., Heiskala, J., Viitanen, T., Simonsson, J., Koskinen, L. (2017).
A 5.3 pJ/op approximate TTA VLIW tailored for machine learning. Microelectronics Journal 61, p. 106-113. DOI

Kulmala L., Žliobaitė I., Nikinmaa E., Nöjd P., Kolari P., Kabiri Koupaei K., Hollmén J., Mäkinen H. (2016). 
Environmental control of growth variation in a boreal Scots pine stand – a data-driven approach. Silva Fennica 50(5), article 1680. link (open access)

Žliobaitė, I., Rinne, J., Toth, A., Mechenich, M., Liu, L., Behrensmeyer, A.K., Fortelius, M. (2016).
Herbivore teeth predict climatic limits in Kenyan ecosystems.
PNAS 113(45), p. 12751-12756, link (open access)

Žliobaitė, I. and Custers, B. (2016).
Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models. 
Artificial Intelligence and Law, 24(2), p. 183-201. DOI  PDF

Fortelius, M., Žliobaitė, I., Kaya, F., Bibi, F., Bobe, R., Leakey, L., Leakey, M., Patterson, D., Rannikko, J., Werdelin, L. (2016).
An ecometric analysis of the fossil mammal record of the Turkana Basin.
Philosophical Transactions B 371(1698), p. 1-13. link (open access)

Žliobaitė, I. and Khokhlov, M. (2016).
Optimal estimates for short horizon travel time prediction in urban areas.
Intelligent Data Analysis 20(6), p. 1459–1475. DOI  PDF

Read, J., Žliobaitė, I. and Hollmén, J. (2016).
Labeling Sensing Data for Mobility Modeling.
Information Systems 57, p. 207-222. DOI  PDF

Žliobaitė, I., Hollmén, J., Koskinen, L., and Teittinen, J. (2014).
Towards hardware-driven design of low-energy algorithms for data analysis.
SIGMOD Record 43(4), p. 15-20.  PDF  DOI   project_poster

Žliobaitė, I., Hollmén, J., H. Junninen. (2014).
Regression models tolerant to massively missing data: a case study in solar radiation nowcasting.
Atmospheric Measurement Techniques Discussions 7, 7137-7174. DOI  PDF
Atmospheric Measurement Techniques 7, 4387-4399. DOI

Krempl, G., Žliobaitė, I., Brzezinski, D.,  Hullermeier, E., Last, M., Lemaire, V., Noack, T., Shaker, A., Sievi, S., Spiliopoulou, M., Stefanowski, J. (2014).
Open Challenges for Data Stream Mining Research.
SIGKDD Explorations 16(1), p. 1-10. DOI  PDF

Žliobaitė, I., Hollmén, J. (2015).
Optimizing regression models for data streams with missing values.
Machine Learning 99(1), p. 47-73.  PDF  DOI

Žliobaitė, I., Budka, M., Stahl, F. (2015).
Towards cost-sensitive adaptation: when is it worth updating your predictive model?
Neurocomputing 150(A), p. 240-249DOI  PDF

Žliobaitė, I., Bifet, A., Read, J., Pfahringer, B., Holmes, G. (2015).
Evaluation methods and decision theory for classification of streaming data with temporal dependence.
Machine Learning 98(3), p. 455-482.  DOI  PDF

Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A. (2014).
A Survey on Concept Drift Adaptation.
ACM Computing Surveys 46(4), Article No. 44. DOI  PDF

Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M. (2014).
Dealing with Concept Drifts in Process Mining.
IEEE Transactions on Neural Networks and Learning Systems 25(1), 154-171. DOI

Žliobaitė, I. (2014).
Controlled Permutations for Testing Adaptive Learning Models.
Knowledge and Information Systems, 39(3), 565-578. DOI  PDF   data

Žliobaitė, I., Bifet, A., Pfahringer, B., Holmes, G. (2014).
Active Learning with Drifting Streaming Data.
IEEE Transactions on Neural Networks and Learning Systems 25(1), p. 27-39. DOI  PDF

Ang, H. H., Gopalkrishnan, V., Žliobaitė, I., Pechenizkiy, M., Hoi, S. C. H. (2013).
Predictive Handling of Asynchronous Concept Drifts in Distributed Environments.
IEEE Transactions on Knowledge and Data Engineering 25(10), p. 2343-2355.  DOI  PDF

Žliobaitė, I. and Gabrys, B. (2014).
Adaptive Preprocessing for Streaming Data.
IEEE Transactions on Knowledge and Data Engineering 26(2), p. 309-321. DOI  PDF

Kamiran, F., Žliobaitė, I. and Calders, T. (2013).
Quantifying explainable discrimination and removing illegal discrimination in automated decision making.
Knowledge and Information Systems 35(3), p. 613-644. DOI  PDF  code

Žliobaitė, I., Bifet, A., Gaber, M., Gabrys, B., Gama, J., Minku, L. and Musial, K. (2012).
Next challenges for adaptive learning systems.
SIGKDD Explorations 14(1), p. 48-55. DOI  PDF

Žliobaitė, I., Bakker, J. and Pechenizkiy, M. (2012).
Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?
Expert Systems with Applications
39(1), p. 806-815. DOI  PDF

Žliobaitė, I. (2011).
Combining similarity in time and space for training set formation under concept drift.

Intelligent Data Analysis 15(4), p. 589-611. DOI  PDF  data

Pechenizkiy, M., Bakker, J., Žliobaitė, I., Ivannikov, A., Karkkainen, T. (2009).
Online Mass Flow Prediction in CFB Boilers with Explicit Detection of Sudden Concept Drift.
SIGKDD Explorations 11(2), p. 109-116. DOI  PDF

Kuncheva, L.I. and Žliobaitė, I. (2009).
On the Window Size for Classification in Changing Environments.
Intelligent Data Analysis 13(6), p. 861-872. DOI  PDF

Book Chapters

Žliobaitė, I., Pechenizkiy, M. and Gama, J. (2016).
An overview of concept drift applications.
Big Data Analysis: New Algorithms for a New Society. Japkowicz, N. and Stefanowski, J. (Eds.), Springer, p. 91-114. DOI  PDF

Kamiran, F. and Žliobaitė, I. (2013).
Explainable and Non-explainable Discrimination in Classification.
Discrimination and Privacy in the Information Society, series: Studies in Applied Philosophy, Epistemology and Rational Ethics, Vol. 3. Custers, B.; Zarsky, T.; Schermer, B.; Calders, T. (Eds.), Springer, p. 155-170. DOI  PDF

Calders, T. and Žliobaitė, I. (2013).
Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures.
Discrimination and Privacy in the Information Society, series: Studies in Applied Philosophy, Epistemology and Rational Ethics, Vol. 3. Custers, B.; Zarsky, T.; Schermer, B.; Calders, T. (Eds.), Springer, p. 43-57DOI  PDF

Žliobaitė, I. (2011).
Three Data Partitioning Strategies for Building Local Classifiers.
Ensembles in Machine Learning Applications, series: Studies in Computational Intelligence, Vol. 373. Valentini, G., Re, M., Okun, O.  (Eds.), Springer, p. 233-250. DOI  PS

Žliobaitė, I. (2007).
Introduction of New Expert and Old Expert Retirement under Concept Drift.
Progress in Pattern Recognition, series: Advances in Computer Vision and Pattern Recognition, XIII. S. Singh, M. Singh (Eds.), Springer, p. 64-74. PDF

Publications in Conference Proceedings

Krilavičius, T., Žliobaitė, I., Simonavičius, H., Jaruševičius, L. (2016).
Predicting respiratory motion for real-time tumour tracking in radiotherapy.
Proc. of IEEE 29th Int. Symposium on Computer-Based Medical Systems (CBMS), p. 7-12. DOI  PDF

Maslov, A., Pechenizkiy, M., Žliobaitė, I. and Kärkkäinen, T. (2016).
Modelling Recurrent Events for Improving Online Change Detection.
Proc. of SIAM Int. Conf. on Data Mining (SDM), p. 549-557. DOI  PDF

Martin Salvador M., Gabrys, B., Žliobaitė, I. (2014). 
Online Detection of Shutdown Periods in Chemical Plants: A Case Study. 
Procedia Computer Science 35, p. 580-588.  DOI

Budka, M., Eastwood, M., Gabrys, B., Kadlec, P., Martin-Salvador, M., Schwan, S., Tsakonas, A.,  and Žliobaitė, I. (2014).
From Sensor Readings to Predictions: on the Process of Developing Practical Soft Sensors.
Proc. of The 13th Int. Symposium on Intelligent Data Analysis, Springer LNCS 8819, p. 49-60. DOI PDF

Žliobaitė, I., Hollmén, J. (2013).
Fault tolerant regression for sensor data.
Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13)Springer LNAI 8188, p. 449-464. PDF  DOI

Bifet, A., Read, J., Žliobaitė, I., Pfahringer, B., Holmes, G. (2013).
Pitfalls in benchmarking data stream classification and how to avoid them. 
Proc. of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECMLPKDD'13)Springer LNAI 8188, p. 465-479. PDF  DOI

Ienco, D., Bifet, A., Žliobaitė, I., Pfahringer, B. (2013).
Clustering Based Active Learning for Evolving Data Streams.
Proc. of the 16th Int. Conf. on Discovery Science (DS'13), Springer LNCS 8140, p. 79-93. PDF  DOI  code

Bifet, A., Read, J., Pfahringer, B., Holmes, G., Žliobaitė, I. (2013).
CD-MOA: Change Detection Framework for Massive Online Analysis. 
Proc. of the 20th Int. Symposium on Intelligent Data Analysis (IDA'13), p. 92-103. DOI  PDF

Apeh, E., Žliobaitė, I., Pechenizkiy, M., Gabrys, B. (2012). 
Predicting Multi-Class Customer Profiles Based on Transactions: a Case Study in Food Sales
Proc. of the 32nd Annual Int. Conf. of the British Computer Society's Specialist Group on Artificial Intelligence (SGAI'12)Research and Development in Intelligent Systems XXIX, p. 213-218. DOI  PDF  PDFlong

Žliobaitė, I., Kamiran, F., Calders, T. (2011).
Handling Conditional Discrimination.
Proc. of the 11th IEEE Int. Conf. on Data Mining (ICDM'11), p. 992 - 1001. DOI  PDF  code

Žliobaitė, I. (2011).
Controlled Permutations for Testing Adaptive Classifiers.
Proc. of the 14th International Conf. on Discovery Science (DS'11) , Springer LNCS 6926, p. 365-379. DOI  PDF  code

Mazhelis, O., Žliobaitė, I., Pechenizkiy, M. (2011).
Context-aware Personal Route Recognition.
Proc. of the 14th International Conf. on Discovery Science (DS'11) , Springer LNCS 6926, p. 221-235. DOI  PDF

Žliobaitė, I., Bifet, A., Pfahringer, B., Holmes, G. (2011).
Active Learning with Evolving Streaming Data.
Proc. of the 21st European Conf. on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'11), Springer LNCS 6913, p. 597-612. DOI  PDF  code

Bose, R.P.J.C., van der Aalst, W.M.P., Žliobaitė, I., Pechenizkiy, M. (2011).
Handling Concept Drift in Process Mining.
Proc. of the 23rd Int. Conf. on Advanced Information Systems Engineering (CAiSE'11), Springer LNCS 6741, p. 391-405. DOI  PDF

Žliobaitė, I. (2011).
Identifying Hidden Contexts in Classification.
Proc. of the 15th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'11), Springer LNAI 6634, p. 277-288. DOI  PDF

Pechenizkiy, M., Vasilyeva, E., Žliobaitė, I., Tesanovic, A., Manev, G. (2010).
Heart Failure Hospitalization Prediction in Remote Patient Management Systems.
In: Dillon et al. (Eds), Proc. of the 23rd International Symposium on Computer-Based Medical Systems (CBMS '10), IEEE Press, p. 44-50. DOI  PDF

Žliobaitė, I., Bakker, J., Pechenizkiy M. (2009).
OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers.
Proc. of the 12th International Conf. on Discovery Science (DS 2009), Springer LNAI 5808, p. 272-286.DOI  PDF

Žliobaite, I. (2009).
Combining Time and Space Similarity for Small Size Learning under Concept Drift.
Proc. of the 18th Int. Symposium on Methodologies for Intelligent Systems (ISMIS'09), Springer LNCS 5722, p. 412-421. DOI  PDF

Žliobaitė, I. (2008).
Expected Classification Error of the Euclidean Linear Classifier under Sudden Concept Drift.
Proc. of the 5th int. conference on Fuzzy Systems and Knowledge Discovery (FSKD'08), IEEE Computer Society: vol 2, p. 29-33. DOI  PDF

Žliobaitė, I. (2007).
Ensemble Learning for Concept Drift Handling – the Role of New Expert.
Poster Proceedings of the 5th int. conf. on Machine Learning and Data Mining in Pattern Recognition (MLDM'07), p. 251-260. PDF

Raudys, Š., Žliobaitė, I. (2006).
The Multi-Agent System for Prediction of Financial Time Series.
Proc. of the 8th int. conf. on Artificial Intelligence and Soft Computing (ICAISC'06), Springer LNAI 4029, p. 653-662. DOI

Raudys, Š., Žliobaitė, I. (2005).
Prediction of Commodity Prices in Rapidly Changing Environments.
Pattern Recognition and Data Mining, proc. of the 3rd int. conf. on Advances in Pattern Recognition (ICAPR'05), Springer LNCS 3686, p. 154-163. DOI

Publications in Workshop Proceedings

Žliobaitė, I. (2015). 
On the relation between accuracy and fairness in binary classification. The 2nd workshop on Fairness, Accountability, and Transparency in Machine Learning (FATML) at ICML'15.  PDF  arXiv

Žliobaitė, I., Mathioudakis, M., Lehtiniemi, T., Parviainen, P., Janhunen, T. (2015). 
Accessibility by public transport predicts residential real estate prices: a case study in Helsinki region. Proc. of the 2nd workshop on Mining Urban Data (MUD2), 65-71PDF

Ienco, D., Pfahringer, B. and Žliobaitė, I. (2014).
High density-focused uncertainty sampling for active learning over evolving stream data.
Proc. of 3rd int. workshop on Big Data Mining, JMLR W&CP 36, p. 133–148. DOI  PDF

Žliobaitė, I. and Hollmén, J. (2014).
Mobile Sensing Data for Urban Mobility Analysis: A Case Study in Preprocessing.
Proc. of Mining Urban Data workshop at EDBT/ICDT, p. 309-314.  PDF data

Žliobaitė, I., Bifet, A., Holmes, G., Pfahringer, B. (2011)
MOA Concept Drift Active Learning Strategies for Streaming Data.
Proc. of the 2nd Workshop on Applications of Pattern Analysis, JMLR Workshop and Conference Proceedings (17), p. 48-55. DOI  PDF

Žliobaitė, I. (2010).
Change with Delayed Labeling: when is it detectable?
Proc. of 2010 IEEE int. conf. on Data Mining Workshops, the 5th Int. workshop on Chance Discovery (IWCD10) at ICDM'10, IEEE Computer Society, p. 843-850. DOI  PDF

Žliobaitė, I., Pechenizkiy, M. (2010).
Learning with Actionable Attributes: Attention – Boundary Cases!
Proc. of 2010 IEEE int. conf. on Data Mining Workshops, Int. workshop on Domain Driven Data Mining (DDDM'10) at ICDM'10, IEEE Computer Society, p. 1021-1028. DOI  PDF
Poster presented at MPS wokshop

Žliobaitė, I. (2010).
Three Data Partitioning Strategies for Building Local Classifiers: an experiment.
Proc. of SUEMA workshop at ECML PKDD'10, p.151-160. PDF slides

Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2009).
Towards Context Aware Food Sales Prediction.
Proc. of 2009 IEEE int. conf. on Data Mining Workshops, int. workshop on Domain Driven Data Mining (DDDM'09), IEEE Computer Society, p. 94-99. DOI  PDF

Žliobaitė, I., Kuncheva, L. (2009).
Determining the Training Window for Small Sample Size Classification with Concept Drift.
Proc. of 2009 IEEE int. conf. on Data Mining Workshops, the 1st int. workshop on Transfer Mining (TM'09), IEEE Computer Society, p. 447-452. DOI  PDF

Bakker, J., Pechenizkiy, M, Žliobaitė, I., Ivannikov, A. and Kärkkäinen, T. (2009).
Handling Outliers and Concept Drift in Online Mass Flow Prediction in CFB Boilers.
Proc. of the 3rd int. workshop on Knowledge Discovery from Sensor Data (SensorKDD’09), p. 13-22. [Best Paper award] DOI  PDF

Published extended abstracts / abstracts

Krilavičius, T., Užupytė, R., Žliobaitė, I., Simonavičius, H. (2013). 
Correlation of external markers and functional targets for respiration compensation in radiotherapy. 
Medical Physics in the Baltic States 11, p. 42-45 slides

Pechenizkiy, M., Žliobaitė, I. (2010).
Handling Concept Drift in Medical Applications: Importance, Challenges and Solutions.
In: Dillon et al. (Eds) Proc. of the 23rd International Symposium on Computer-Based Medical Systems (CBMS '10), IEEE Press, p. 5. [abstract] DOI

Žliobaitė, I., Bakker, J., Pechenizkiy, M. (2009).
Context Aware Sales Prediction.
Proc. of the 21st Benelux conference on Artificial Intelligence (BNAIC'09), p. 449-450. [extended abstract] PDF

Kuncheva, L., Žliobaitė, I. (2008).
Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift.
SSPR/SPR'08, Springer LNCS, p: 4.  [abstract, invited talk] DOI 

Edited Proceedings and Editorials

Bielza, C., Gama, J., Jorge, A. and Žliobaitė, I. (2015).
Guest editors introduction: special issue of the ECMLPKDD 2015 journal track. 
Data Min. Knowl. Discov. 29(5), 1113-1115.

Bielza, C., Gama, J., Jorge, A. and Žliobaitė, I. (2015).
Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track. 
Machine Learning 100(2-3), p.157-159.

Krempl, G., Žliobaitė, I., Wang, Y. and Forman, G. (editors).
Real-World Challenges for Data Stream Mining, Proceedings of the 1st International Workshop on Real-World Challenges for Data Stream Mining (RealStream 2013) in conjunction with ECMLPKDD 2013. ISBN 978-3-940961-97. PDF

Calders, T. and Žliobaitė, I. (2012). Preface: International workshop on discrimination and privacy-aware data mining. Proceedings of the 12th IEEE International Conference on Data Mining Workshops (ICDMW). DOI

Pechenizkiy, M. and Žliobaitė, I. (2012). Introduction to the special issue on handling concept drift in adaptive information systems. Evolving systems 4(1), p. 1-2. DOI

Khan, L., Pechenizkiy, M., Žliobaitė, I. (2011). Preface to the Handling Concept Drift and Reoccurring Contexts in Adaptive Information Systems Workshop. Proceedings of the 11th IEEE International Conference on Data Mining Workshops. DOI

Pechenizkiy, M., Žliobaitė, I. (editors). 
Proceedings of the First International Workshop on Handling Concept Drift in Adaptive Information Systems: Importance, Challenges and Solutions (HaCDAIS 2010) in conjunction with ECML PKDD 2010. PDF

Technical Reports and Non Peer-Reviewed

Žliobaitė, I., Khokhlov, M. (2015).
Optimal estimates for short horizon travel time prediction in urban areas.
arXiv: 1507.08444  DOI

Krilavičius, T., Žliobaitė, I. Simonavičius, H., Jaruševičius, L. (2015).
Predicting respiratory motion for real-time tumour tracking in radiotherapy. 
arXiv: 1508.00749  DOI

Bose, R.P.J.C, van der Aalst, W. M. P.Žliobaitė, I. and Pechenizkiy, M. (2013). 
Dealing With Concept Drifts in Process Mining: A Case Study in a Dutch Municipality.
BPM Center Report BPM-13-13, BPMcenter.org  PDF

Žliobaitė, I. (2013). 
How good is the Electricity benchmark for evaluating concept drift adaptation
arXiv: 1301.3524  DOI   PDF

Žliobaitė, I. and Pechenizkiy, M. (2010).
Reference Framework for Handling Concept Drift: An Application Perspective.
Technical report, Eindhoven University of Technology PDF

Žliobaitė, I. and Kuncheva, L. (2010).
Theoretical Window Size for Classification in the Presence of Sudden Concept Drift.
Technical Report, CS-TR-001-2010, Bangor University, UK PDF

Žliobaitė, I. (2009).
Learning under Concept Drift: an Overview.
Vilnius University, Technical Report PDF 
arXiv: 1010.4784 DOI

Žliobaitė, I. and Krilavičius, T. (2009).
CLAN: Clustering for Credit Risk Assessment.
An entry to PAKDD 2009 Data Mining Competition. PDF

Žliobaitė, I. (2009).
On Use of Historical Information under Sudden and Gradual Concept Drift. Vilnius University,
Faculty of Mathematics and Informatics, Technical Report 2009-02. PDF

PhD Thesis

Žliobaitė, I. (2010).
Adaptive Training Set Formation.
Vilnius University, Lithuania. PDF slides
Thesis visualized by Wordle