Mykola Pechenizkiy

Bio: Mykola Pechenizkiy is an academic researcher from Eindhoven University of Technology. The author has contributed to research in topics: Artificial neural network & Feature selection. The author has an hindex of 36, co-authored 299 publications receiving 7731 citations. Previous affiliations of Mykola Pechenizkiy include Sahand University of Technology & University of Twente.

A survey on concept drift adaptation

Abstract: Concept drift primarily refers to an online supervised learning scenario when the relation between the input data and the target variable changes over time. Assuming a general knowledge of supervised learning in this article, we characterize adaptive learning processes; categorize existing strategies for handling concept drift; overview the most representative, distinct, and popular techniques and algorithms; discuss evaluation methodology of adaptive algorithms; and present a set of illustrative applications. The survey covers the different facets of concept drift in an integrated way to reflect on the existing scattered state of the art. Thus, it aims at providing a comprehensive introduction to the concept drift adaptation for researchers, industry analysts, and practitioners.

Building Classifiers with Independency Constraints

Abstract: In this paper we study the problem of classifier learning where the input data contains unjustified dependencies between some data attributes and the class label. Such cases arise for example when the training data is collected from different sources with different labeling criteria or when the data is generated by a biased decision process. When a classifier is trained directly on such data, these undesirable dependencies will carry over to the classifier’s predictions. In order to tackle this problem, we study the classification with independency constraints problem: find an accurate model for which the predictions are independent from a given binary attribute. We propose two solutions for this problem and present an empirical validation.

Handbook of Educational Data Mining

Abstract: Preface, Joseph E. Beck Introduction, Cristobal Romero, Sebastian Ventura, Mykola Pechenizkiy, and Ryan Baker Basic Techniques, Surveys, and Tutorials Visualization in Educational Environments, Riccardo Mazza Basics of Statistical Analysis of Interactions Data from Web-Based Learning Environments, Judy Sheard A Data Repository for the EDM Community: The PSLC DataShop, Kenneth R. Koedinger, Ryan Baker, Kyle Cunningham, Alida Skogsholm, Brett Leber, and John Stamper Classifiers for EDM, Wilhelmiina Hamalainen and Mikko Vinni Clustering Educational Data, Alfredo Vellido, Felix Castro, and Angela Nebot Association Rule Mining in Learning Management Systems, Enrique Garcia, Cristobal Romero, Sebastian Ventura, Carlos de Castro, and Toon Calders Sequential Pattern Analysis of Learning Logs: Methodology and Applications, Mingming Zhou, Yabo Xu, John C. Nesbit, and Philip H. Winne Process Mining from Educational Data, Nikola Trcka, Mykola Pechenizkiy, and Wil van der Aalst Modeling Hierarchy and Dependence among Task Responses in EDM, Brian W. Junker Case Studies Novel Derivation and Application of Skill Matrices: The q-Matrix Method, Tiffany Barnes EDM to Support Group Work in Software Development Projects, Judy Kay, Irena Koprinska, and Kalina Yacef Multi-Instance Learning versus Single-Instance Learning for Predicting the Student's Performance, Amelia Zafra, Cristobal Romero, and Sebastian Ventura A Response-Time Model for Bottom-Out Hints as Worked Examples, Benjamin Shih, Kenneth R. Koedinger, and Richard Scheines Automatic Recognition of Learner Types in Exploratory Learning Environments, Saleema Amershi and Cristina Conati Modeling Affect by Mining Students' Interactions within Learning Environments, Manolis Mavrikis, Sidney D'Mello, Kaska Porayska-Pomsta, Mihaela Cocea, and Art Graesser Measuring Correlation of Strong Symmetric Association Rules in Educational Data, Agathe Merceron and Kalina Yacef Data Mining for Contextual Educational Recommendation and Evaluation Strategies, Tiffany Y. Tang and Gordon G. McCalla Link Recommendation in E-Learning Systems Based on Content-Based Student Profiles, Daniela Godoy and Analia Amandi Log-Based Assessment of Motivation in Online Learning, Arnon Hershkovitz and Rafi Nachmias Mining Student Discussions for Profiling Participation and Scaffolding Learning, Jihie Kim, Erin Shaw, and Sujith Ravi Analysis of Log Data from a Web-Based Learning Environment: A Case Study, Judy Sheard Bayesian Networks and Linear Regression Models of Students' Goals, Moods, and Emotions, Ivon Arroyo, David G. Cooper, Winslow Burleson, and Beverly P. Woolf Capturing and Analyzing Student Behavior in a Virtual Learning Environment: A Case Study on Usage of Library Resources, David Masip, Julia Minguillon, and Enric Mor Anticipating Student's Failure as soon as Possible, Claudia Antunes Using Decision Trees for Improving AEH Courses, Javier Bravo, Cesar Vialardi, and Alvaro Ortigosa Validation Issues in EDM: The Case of HTML-Tutor and iHelp, Mihaela Cocea and Stephan Weibelzahl Lessons from Project LISTEN's Session Browser, Jack Mostow, Joseph E. Beck, Andrew Cuneo, Evandro Gouvea, Cecily Heiner, and Octavio Juarez Using Fine-Grained Skill Models to Fit Student Performance with Bayesian Networks, Zachary A. Pardos, Neil T. Heffernan, Brigham S. Anderson, and Cristina L. Heffernan Mining for Patterns of Incorrect Response in Diagnostic Assessment Data, Tara M. Madhyastha and Earl Hunt Machine-Learning Assessment of Students' Behavior within Interactive Learning Environments, Manolis Mavrikis Learning Procedural Knowledge from User Solutions to Ill-Defined Tasks in a Simulated Robotic Manipulator, Philippe Fournier-Viger, Roger Nkambou, and Engelbert Mephu Nguifo Using Markov Decision Processes for Automatic Hint Generation, Tiffany Barnes, John Stamper, and Marvin Croy Data Mining Learning Objects, Manuel E. Prieto, Alfredo Zapata, and Victor H. Menendez An Adaptive Bayesian Student Model for Discovering the Student's Learning Style and Preferences, Cristina Carmona, Gladys Castillo, and Eva Millan Index

Predicting Students Drop Out: A Case Study

Abstract: The monitoring and support of university freshmen is considered very important at many educational institutions. In this paper we describe the results of the educational data mining case study aimed at predicting the Electrical Engineering (EE) students drop out after the first semester of their studies or even before they enter the study program as well as identifying success-factors specific to the EE program. Our experimental results show that rather simple and intuitive classifiers (decision trees) give a useful result with accuracies between 75 and 80%. Besides, we demonstrate the usefulness of cost-sensitive learning and thorough analysis of misclassifications, and show a few ways of further prediction improvement without having to collect additional data about the students.

Discrimination Aware Decision Tree Learning

Abstract: Recently, the following discrimination aware classification problem was introduced: given a labeled dataset and an attribute B, find a classifier with high predictive accuracy that at the same time does not discriminate on the basis of the given attribute B. This problem is motivated by the fact that often available historic data is biased due to discrimination, e.g., when B denotes ethnicity. Using the standard learners on this data may lead to wrongfully biased classifiers, even if the attribute B is removed from training data. Existing solutions for this problem consist in “cleaning away” the discrimination from the dataset before a classifier is learned. In this paper we study an alternative approach in which the non-discrimination constraint is pushed deeply into a decision tree learner by changing its splitting criterion and pruning strategy. 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.

I and i

Abstract: There is, I think, something ethereal about i —the square root of minus one. I remember first hearing about it at school. It seemed an odd beast at that time—an intruder hovering on the edge of reality. Usually familiarity dulls this sense of the bizarre, but in the case of i it was the reverse: over the years the sense of its surreal nature intensified. It seemed that it was impossible to write mathematics that described the real world in …

Principles of Neural Science

Abstract: I have developed "tennis elbow" from lugging this book around the past four weeks, but it is worth the pain, the effort, and the aspirin. It is also worth the (relatively speaking) bargain price. Including appendixes, this book contains 894 pages of text. The entire panorama of the neural sciences is surveyed and examined, and it is comprehensive in its scope, from genomes to social behaviors. The editors explicitly state that the book is designed as "an introductory text for students of biology, behavior, and medicine," but it is hard to imagine any audience, interested in any fragment of neuroscience at any level of sophistication, that would not enjoy this book. The editors have done a masterful job of weaving together the biologic, the behavioral, and the clinical sciences into a single tapestry in which everyone from the molecular biologist to the practicing psychiatrist can find and appreciate his or

Random graphs

Abstract: We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.