Indre Zliobaite

发表

Controlled Permutations for Testing Adaptive Classifiers

Indre Zliobaite, I. Žliobaitė, Indrė Žliobaitė, 2011, Discovery Science.

Labeling sensing data for mobility modeling

Jesse Read, Indre Zliobaite, Jaakko Hollmén, 2016, Inf. Syst..

A survey of evaluation methods for personal route and destination prediction from mobility traces

Jesse Read, Tuukka Tolvanen, Indre Zliobaite, 2018, Wiley Interdiscip. Rev. Data Min. Knowl. Discov..

Learning under Concept Drift: an Overview pdf

Indre Zliobaite, I. Žliobaitė, 2010, ArXiv.

Towards Context Aware Food Sales Prediction

Mykola Pechenizkiy, Indre Zliobaite, Jorn Bakker, 2009, 2009 IEEE International Conference on Data Mining Workshops.

Ensemble Learning for Concept Drift Handling - the Role of New Expert

Indre Zliobaite, I. Žliobaitė, 2007, MLDM Posters.

Dealing With Concept Drifts in Process Mining

Mykola Pechenizkiy, Wil M. P. van der Aalst, R. P. Jagadeesh Chandra Bose, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Mobile Sensing Data for Urban Mobility Analysis: A Case Study in Preprocessing

Indre Zliobaite, Jaakko Hollmén, 2014, EDBT/ICDT Workshops.

Open challenges for data stream mining research

Eyke Hüllermeier, Myra Spiliopoulou, Mark Last, 2014, SKDD.

Three Data Partitioning Strategies for Building Local Classifiers

Indre Zliobaite, 2011, Ensembles in Machine Learning Applications.

Indre Zliobaite, I. Žliobaitė, Indrė Žliobaitė, 2011, Intell. Data Anal..

CD-MOA: Change Detection Framework for Massive Online Analysis

Geoff Holmes, Jesse Read, Albert Bifet, 2013, IDA.

Handling Concept Drift in Process Mining

Mykola Pechenizkiy, Wil M. P. van der Aalst, R. P. Jagadeesh Chandra Bose, 2011, CAiSE.

Adaptive Preprocessing for Streaming Data

Bogdan Gabrys, Indre Zliobaite, I. Žliobaitė, 2014, IEEE Transactions on Knowledge and Data Engineering.

Predictive User Modeling with Actionable Attributes pdf

Mykola Pechenizkiy, Indre Zliobaite, 2013, ArXiv.

Pitfalls in Benchmarking Data Stream Classification and How to Avoid Them

Geoff Holmes, Jesse Read, Albert Bifet, 2013, ECML/PKDD.

Beating the baseline prediction in food sales: How intelligent an intelligent predictor is?

Mykola Pechenizkiy, Indre Zliobaite, Jorn Bakker, 2012, Expert Syst. Appl..

Accessibility by Public Transport Predicts Residential Real Estate Prices: A Case Study in Helsinki Region

Michael Mathioudakis, Indre Zliobaite, Tomi Janhunen, 2015, MUD@ICML.

Active Learning With Drifting Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2014, IEEE Transactions on Neural Networks and Learning Systems.

Fairness-aware machine learning: a perspective pdf

Indre Zliobaite, I. Žliobaitė, Indrė Žliobaitė, 2017, ArXiv.

MOA Concept Drift Active Learning Strategies for Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2011, WAPA.

Expected Classification Error of the Euclidean Linear Classifier under Sudden Concept Drift

Indre Zliobaite, 2008, 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery.

BLPA: Bayesian learn-predict-adjust method for online detection of recurrent changepoints

Mykola Pechenizkiy, Tommi Kärkkäinen, Yulong Pei, 2017, 2017 International Joint Conference on Neural Networks (IJCNN).

How good is the Electricity benchmark for evaluating concept drift adaptation pdf

Indre Zliobaite, I. Žliobaitė, 2013, ArXiv.

Clustering Based Active Learning for Evolving Data Streams

Dino Ienco, Albert Bifet, Bernhard Pfahringer, 2013, Discovery Science.

A survey on concept drift adaptation

João Gama, Abdelhamid Bouchachia, Albert Bifet, 2014, ACM Comput. Surv..

Optimal estimates for short horizon travel time prediction in urban areas

Indre Zliobaite, Mikhail Khokhlov, I. Žliobaitė, 2015, Intell. Data Anal..

Concept drift over geological times: predictive modeling baselines for analyzing the mammalian fossil record

Indre Zliobaite, 2018, Data Mining and Knowledge Discovery.

Linear Discriminant Classifier (LDC) for Streaming Data with Concept Drift

Ludmila I. Kuncheva, Indre Zliobaite, 2008, SSPR/SPR.

Why Unbiased Computational Processes Can Lead to Discriminative Decision Procedures

Toon Calders, Indre Zliobaite, I. Žliobaitė, 2013, Discrimination and Privacy in the Information Society.

Controlled permutations for testing adaptive learning models

Indre Zliobaite, 2013, Knowledge and Information Systems.

The Multi-Agent System for Prediction of Financial Time Series

Sarunas Raudys, Indre Zliobaite, 2006, ICAISC.

Handling concept drift in medical applications: Importance, challenges and solutions

Mykola Pechenizkiy, Indre Zliobaite, 2010, 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS).

Heart failure hospitalization prediction in remote patient management systems

Mykola Pechenizkiy, Aleksandra Tesanovic, Indre Zliobaite, 2010, 2010 IEEE 23rd International Symposium on Computer-Based Medical Systems (CBMS).

Measuring discrimination in algorithmic decision making

Indre Zliobaite, I. Žliobaitė, Indrė Žliobaitė, 2017, Data Mining and Knowledge Discovery.

Predicting Multi-class Customer Profiles Based on Transactions: a Case Study in Food Sales

Mykola Pechenizkiy, Edward Tersoo Apeh, Bogdan Gabrys, 2012, SGAI Conf..

Using sensitive personal data may be necessary for avoiding discrimination in data-driven decision models

Indre Zliobaite, Bart Custers, I. Žliobaitė, 2016, Artificial Intelligence and Law.

Optimizing regression models for data streams with missing values

Indre Zliobaite, Jaakko Hollmén, 2014, Machine Learning.

Proceedings of the 2nd International Workshop on Mining Urban Data co-located with 32nd International Conference on Machine Learning (ICML 2015), Lille, France, July 11th, 2015

Michael Mathioudakis, Indre Zliobaite, Tomi Janhunen, 2015, MUD@ICML.

Assessing the impact of a health intervention via user-generated Internet content

Richard Pebody, Ingemar Johansson, Elad Yom-Tov, 2015, Data Mining and Knowledge Discovery.

Prediction of Commodity Prices in Rapidly Changing Environments

Sarunas Raudys, Indre Zliobaite, 2005, ICAPR.

Predicting Respiratory Motion for Real-Time Tumour Tracking in Radiotherapy

Tomas Krilavicius, Indre Zliobaite, Henrikas Simonavicius, 2015, 2016 IEEE 29th International Symposium on Computer-Based Medical Systems (CBMS).

Identifying Hidden Contexts in Classification

Indre Zliobaite, I. Žliobaitė, 2011, PAKDD.

Evaluation methods and decision theory for classification of streaming data with temporal dependence

Geoff Holmes, Jesse Read, Albert Bifet, 2015, Machine Learning.

A survey on measuring indirect discrimination in machine learning pdf

Indre Zliobaite, I. Žliobaitė, Indrė Žliobaitė, 2015, ArXiv.

Context-Aware Personal Route Recognition

Mykola Pechenizkiy, Oleksiy Mazhelis, Indre Zliobaite, 2011, Discovery Science.

Active Learning with Evolving Streaming Data

Geoff Holmes, Albert Bifet, Bernhard Pfahringer, 2011, ECML/PKDD.

From Sensor Readings to Predictions: On the Process of Developing Practical Soft Sensors

Athanasios Tsakonas, Bogdan Gabrys, Marcin Budka, 2014, IDA.

A note on adjusting $R^2$ for using with cross-validation pdf

Nikolaj Tatti, Indre Zliobaite, Nikolaj Tatti, 2016, ArXiv.

Online mass flow prediction in CFB boilers with explicit detection of sudden concept drift

Mykola Pechenizkiy, Tommi Kärkkäinen, Indre Zliobaite, 2010, SKDD.

High density-focused uncertainty sampling for active learning over evolving stream data

Dino Ienco, Bernhard Pfahringer, Indre Zliobaite, 2014, BigMine.

Preface to the 2nd International Workshop on Handling Concept Drift in Adaptive Information Systems

Latifur Khan, Mykola Pechenizkiy, Indre Zliobaite, 2011, IEEE ICDM 2011.

Introduction to the special issue on handling concept drift in adaptive information systems

Mykola Pechenizkiy, Indre Zliobaite, 2013, Evol. Syst..

Dealing with concept drifts in process mining : a case study in a Dutch municipality

Mykola Pechenizkiy, van der Wmp Wil Aalst, Indre Zliobaite, 2013 .

Predictive Handling of Asynchronous Concept Drifts in Distributed Environments

Mykola Pechenizkiy, Steven C. H. Hoi, Vivekanand Gopalkrishnan, 2013, IEEE Transactions on Knowledge and Data Engineering.

Towards Hardware-driven Design of Low-energy Algorithms for Data Analysis

Lauri Koskinen, Indre Zliobaite, Jaakko Hollmén, 2015, SGMD.

On the window size for classification in changing environments

Ludmila I. Kuncheva, Indre Zliobaite, L. Kuncheva, 2009, Intell. Data Anal..

Guest editors introduction: special issue of the ECMLPKDD 2015 journal track

Concha Bielza, João Gama, Alípio Mário Jorge, 2015, Data Mining and Knowledge Discovery.

Fault Tolerant Regression for Sensor Data

Indre Zliobaite, Jaakko Hollmén, 2013, ECML/PKDD.

Modelling Recurrent Events for Improving Online Change Detection

Mykola Pechenizkiy, Tommi Kärkkäinen, Indre Zliobaite, 2016, SDM.

Change with Delayed Labeling: When is it Detectable?

Indre Zliobaite, I. Žliobaitė, Indrė Žliobaitė, 2010, 2010 IEEE International Conference on Data Mining Workshops.

Determining the Training Window for Small Sample Size Classification with Concept Drift

Ludmila I. Kuncheva, Indre Zliobaite, L. Kuncheva, 2009, 2009 IEEE International Conference on Data Mining Workshops.

Guest Editors introduction: special issue of the ECMLPKDD 2015 journal track

Concha Bielza, João Gama, Alípio Mário Jorge, 2015, Machine Learning.

A 5.3 pJ/op approximate TTA VLIW tailored for machine learning

Lauri Koskinen, Timo Viitanen, Indre Zliobaite, 2017, Microelectron. J..

Handling outliers and concept drift in online mass flow prediction in CFB boilers

Mykola Pechenizkiy, Tommi Kärkkäinen, Indre Zliobaite, 2009, SensorKDD '09.

Next challenges for adaptive learning systems

Katarzyna Musial, Mohamed Medhat Gaber, João Gama, 2012, SKDD.

Combining Time and Space Similarity for Small Size Learning under Concept Drift

Indre Zliobaite, I. Žliobaitė, Indrė Žliobaitė, 2009, ISMIS.

Online Detection of Shutdown Periods in Chemical Plants: A Case Study

Bogdan Gabrys, Indre Zliobaite, Manuel Martin Salvador, 2014, KES.

On the relation between accuracy and fairness in binary classification pdf

Indre Zliobaite, I. Žliobaitė, Indrė Žliobaitė, 2015, ArXiv.

Handling Conditional Discrimination

Toon Calders, Indre Zliobaite, Faisal Kamiran, 2011, 2011 IEEE 11th International Conference on Data Mining.

OMFP: An Approach for Online Mass Flow Prediction in CFB Boilers

Mykola Pechenizkiy, Indre Zliobaite, Jorn Bakker, 2009, Discovery Science.

Towards cost-sensitive adaptation: When is it worth updating your predictive model?

Marcin Budka, Indre Zliobaite, Frederic T. Stahl, 2015, Neurocomputing.

Learning with Actionable Attributes: Attention -- Boundary Cases!

Mykola Pechenizkiy, Indre Zliobaite, I. Žliobaitė, 2010, 2010 IEEE International Conference on Data Mining Workshops.

Recommender Systems Meet Species Distribution Modelling

Indre Zliobaite, 2021, Perspectives@RecSys.

Redescription mining for analyzing local limiting conditions: A case study on the biogeography of large mammals in China and southern Asia

Esther Galbrun, Indre Zliobaite, Hui Tang, 2021, Ecol. Informatics.