Research themes

Computational palaeobiology, ecometrics

Machine learning with evolving data

Evolving data: position papers and surveys

Evolving data: evaluation methodologies

Evolving data: active learning


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. 27-39DOI  PDF

Evolving data: cost-sensitive adaptation


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

Evolving data: preprocessing


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

Evolving data: missing values


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

Evolving data: handling concept drift


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

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

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

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

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 

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 

Evolving data: change detection


Maslov, A., Pechenizkiy, M., Pei, Žliobaitė, I. Shklyaev, A., Kärkkäinen, T., Hollmén, J. (2017). BLPA: Bayesian Learn-Predict-Adjust Method for Online Detection of Recurrent Changepoints. 
Proc. of the 30th Int. Joint Conf. on Neural Networks (IJCNN). In press. 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

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

Data science applications


Forestry and atmospheric sciences


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

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, 7137-7174DOI  PDF
Atmospheric Measurement Techniques 7, 4387-4399DOI

Industrial engineering and energy


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. 49-60DOI 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

Hardware design


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

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

Traffic and mobility


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

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

Process analysis


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

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


Sales prediction and customer profiling


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

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. 213-218DOI  PDF  PDFlong


Medicine


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

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., 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 


Transparency and accountability in machine learning


Measuring discrimination in algorithmic decision making. PDF DOI
Žliobaitė, I. (2017). Data Mining and Knowledge Discovery 31(4), 1060-1089

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

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

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., 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







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