Class Imbalance and Active Learning

2013-01-01
Attenberg, Josh
Ertekin Bolelli, Şeyda
Show full item record
120
views
0
downloads
This chapter focuses on the interaction between active learning (AL) and class imbalance, discussing (i) AL techniques designed specifically for dealing with imbalanced settings, (ii) strategies that leverage AL to overcome the deleterious effects of class imbalance, (iii) how extreme class imbalance can prevent AL systems from selecting useful examples, and alternatives to AL in these cases. The intent of the chapter is to present a comprehensive analysis of this interplay of AL and class imbalance. The chapter provides the reader with a brief overview of some alternative techniques for active data acquisition for predictive model construction in a cost‐restrictive setting.
Data acquisition, Learning (artificial intelligence)
https://onlinelibrary.wiley.com/doi/book/10.1002/9781118646106
https://hdl.handle.net/11511/79972
Department of Computer Engineering, Book / Book chapter

Suggestions

Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation
Çelikkanat, Hande; Orhan, Guner; Pugeault, Nicolas; Guerin, Frank; Şahin, Erol; Kalkan, Sinan (Institute of Electrical and Electronics Engineers (IEEE), 2016-03-01)
In this paper, we formalize and model context in terms of a set of concepts grounded in the sensorimotor interactions of a robot. The concepts are modeled as a web using Markov Random Field (MRF), inspired from the concept web hypothesis for representing concepts in humans. On this concept web, we treat context as a latent variable of Latent Dirichlet Allocation (LDA), which is a widely-used method in computational linguistics for modeling topics in texts. We extend the standard LDA method in order to make ...
Stable controller design for T-S fuzzy systems based on Lie algebras
Banks, SP; Gurkan, E; Erkmen, İsmet (Elsevier BV, 2005-12-01)
In this paper, we study the stability of fuzzy control systems of Takagi-Sugeno-(T-S) type based on the classical theory of Lie algebras. T-S fuzzy systems are used to model nonlinear systems as a set of rules with consequents of the type x(t) = A(l)x (t) + B(l)u (t). We conduct the stability analysis of such T-S fuzzy models using the Lie algebra LA generated by the A(l) matrices of these subsystems for each rule in the rule base. We first develop our approach of stability analysis for a commuting algebra ...
Social Justice in Turkish Education System: Issues and Interventions
Kondakçı, Yaşar (Springer, London/Berlin , 2020-01-01)
This chapter discusses the research and practice in social justice in education inTurkey. Economic, social, political, and demographic developments that createand perpetuate disparities in different societies are equally valid for Turkey.In order to capture the state of social justice in education first, Turkey’s social,demographic, and economic status of the country was discussed. Turkey has arelatively young population, which increases the demand for public education.The key figures on the schooling rates...
Emotion analysis of Turkish texts by using machine learning methods
Boynukalın, Zeynep; Karagöz, Pınar; Department of Computer Engineering (2012)
Automatically analysing the emotion in texts is in increasing interest in today’s research fields. The aim is to develop a machine that can detect type of user’s emotion from his/her text. Emotion classification of English texts is studied by several researchers and promising results are achieved. In this thesis, an emotion classification study on Turkish texts is introduced. To the best of our knowledge, this is the first study on emotion analysis of Turkish texts. In English there exists some well-defined...
Student Engagement in Blended Learning Environments with Lecture-Based and Problem-Based Instructional Approaches
Delialioğlu, Ömer (NATL YUNLIN UNIV SCIENCE & TECHNOLOGY, TAIWAN, NO 123, SECTION 3, DAXUE RD, DOULIU CITY, YUNKIN COUNTY 64002, TAIWAN, 2012-7)
This study investigates how blending of different instructional approaches with technology affects students' engagement. A computer networks course was designed and implemented for the first eight weeks of the semester as a lecture-based blended learning environment and for the second eight weeks of the semester as a problem-based blended learning environment. A single group repeated measures research design was carried out to understand if there are significant differences in measures of student engagement...
Citation Formats
J. Attenberg and Ş. Ertekin Bolelli, Class Imbalance and Active Learning. 2013, p. 149.
METU IIS - Integrated Information Service open@metu.edu.tr