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A survey on concept drift adaptation
- João Gama, I. Žliobaitė, A. Bifet, Mykola Pechenizkiy, A. Bouchachia
- Computer ScienceACM Comput. Surv.
- 1 March 2014
The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art and aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.
Discrimination Aware Decision Tree Learning
- F. Kamiran, T. Calders, Mykola Pechenizkiy
- Computer ScienceIEEE International Conference on Data Mining
- 13 December 2010
Experimental evaluation shows that the proposed approach advances the state-of-the-art in the sense that the learned decision trees have a lower discrimination than models provided by previous methods, with little loss in accuracy.
Building Classifiers with Independency Constraints
- T. Calders, F. Kamiran, Mykola Pechenizkiy
- Computer ScienceIEEE International Conference on Data Mining…
- 1 December 2009
This paper studies the classification with independency constraints problem: find an accurate model for which the predictions are independent from a given binary attribute and proposes two solutions and presents an empirical validation.
AH 12 years later: a comprehensive survey of adaptive hypermedia methods and techniques
This paper examines adaptation questions stated in the very beginning of the AH era and elaborate on their recent interpretations, reconsider design issues for application independent generic AHS, review open questions of system extensibility introduced in adjacent research fields and try to come up with an up-to-date taxonomy of adaptation techniques.
Diversity in search strategies for ensemble feature selection
Dynamic integration of classifiers for handling concept drift
Handbook of Educational Data Mining
A Response-Time Model for Bottom-Out Hints as Worked Examples and Recommendation in E-Learning Systems Based on Content-Based Student Profiles.
What's Your Current Stress Level? Detection of Stress Patterns from GSR Sensor Data
- J. Bakker, Mykola Pechenizkiy, N. Sidorova
- Computer ScienceIEEE 11th International Conference on Data Mining…
- 11 December 2011
This paper formulates the problem of stress identification and categorization from the sensor data stream mining perspective, considers a reductionist approach for arousal identification as a drift detection task, and highlights the major problems of dealing with GSR data.
Handling Concept Drift in Process Mining
This paper presents an approach to analyze second-order dynamics in process mining and has been implemented in ProM1 and evaluated by analyzing an evolving process.
Predicting Students Drop Out: A Case Study
The experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%, and the usefulness of cost-sensitive learning and thorough analysis of misclassifications is demonstrated.