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Conference proceeding
Transductive Learning Algorithms for Nonstationary Environments
2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
01 Jan 2012
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Many traditional supervised machine learning approaches, either on-line or batch based, assume that data are sampled from a fixed yet unknown source distribution. Most incremental learning algorithms also make the same assumption, even though new data are presented over periods of time. Yet, many real-world problems are characterized by data whose distribution change over time, which implies that a classifier may no longer be reliable on future data, a problem commonly referred to as concept drift or learning in nonstationary environments. The issue is further complicated when the problem requires prediction from data obtained at a future time step, for which the labels are not yet available. In this work, we present a transductive learning methodology that uses probabilistic models to aid in computing ensemble classifier voting weights. Assuming the drift is limited in nature, the proposed approach exploits a probabilistic estimate to determine the class responsibility of components in a Gaussian mixture model (GMM), generated from labeled and unlabeled data. A general error bound is provided based on the ensemble decision, the probabilistic estimate of the GMM, and the true labeling function, which, unfortunately is never actually known.
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Details
- Title
- Transductive Learning Algorithms for Nonstationary Environments
- Creators
- Gregory Ditzler - Drexel UniversityGail Rosen - Drexel UniversityRobi Polikar - Rowan UniversityIEEE
- Publication Details
- 2012 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
- Series
- IEEE International Joint Conference on Neural Networks (IJCNN)
- Publisher
- IEEE
- Number of pages
- 8
- Grant note
- 0845827 / National Science Foundation (NSF) CAREER award; National Science Foundation (NSF); NSF - Office of the Director (OD) SC004335 / Department of Energy Award; United States Department of Energy (DOE) 1120622 / NSF Award; National Science Foundation (NSF) ECCS-0926159 / NSF; National Science Foundation (NSF)
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Electrical and Computer Engineering
- Web of Science ID
- WOS:000309341300132
- Scopus ID
- 2-s2.0-84865066024
- Other Identifier
- 991019170600704721
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- Collaboration types
- Domestic collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence