Journal Publications

NEW Kamiran, F., Žliobaitė, I. and Calders, T. (2012).
Quantifying explainable discrimination and removing illegal discrimination in automated decision making.
Knowledge and Information Systems, accepted.

NEW Žliobaitė, I., Bifet, A., Gaber, M., Gabrys, B., Gama, J., Minku, L. and Musial, K. (2012).
Next challenges for adaptive learning systems.
SIGKDD Explorations, accepted. 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

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

NEW Kamiran, F. and Žliobaitė, I. (2012).
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, to appear. PDF

NEW Calders, T. and Žliobaitė, I. (2012).
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, to appear. 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

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  resources

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  resources

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  resources

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., 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. 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

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

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