Book Chapters
Adaptive classifiers for nonstationary environments
C. Alippi, G. Boracchi, G. Ditzler, R. Polikar, and M. Roveri
Contemporary Issues in Systems, Science and Engineering, IEEE/Wiley Press Book Series, 2015.
Feature selection for metagenomic data analysis
G. Ditzler, Y. Lan, J.-L. Bouchot, and G. Rosen
Encyclopedia of Metagenomics, 2014.
Advances in machine learning for processing and comparison of metagenomic data
J.-L. Bouchot, W. Trimble, G. Ditzler, Y. Lan, S. Essinger, and G. Rosen
Computational Systems Biology, Springer, 2014.
Journals
A Sequential Learning Approach for Scaling up Filter-Based Feature Subset Selection
G. Ditzler, N. Bouaynaya, and R. Shterenberg
Signal Processing, 2018, vol. 144, pp. 265-270.
Improvements to Scalable Online Feature Selection Using Bagging and Boosting
G. Ditzler, J. LaBarck, J. Ritchie, G. Rosen, and R. Polikar
IEEE Transactions on Neural Networks and Learning Systems, 2017.
A Sequential Learning Approach for Scaling up Filter-Based Feature Subset Selection
G. Ditzler, R. Polikar, and G. Rosen
IEEE Transactions on Neural Networks and Learning Systems, 2017.
Fizzy: Feature selection for metagenomics
G. Ditzler, J. Calvin Morrison, Y. Lan, and G. Rosen
BMC Bioinformatics, 2015, vol 16, no. 358. [code ]
Multi-Layer and Recursive Neural Networks for Metagenomic Classification
G. Ditzler, R. Polikar, and G. Rosen
IEEE Transactions on Nanobioscience, 2015, vol. 14, no. 6, pp. 608-616.
Adaptive strategies for learning in nonstationary environments: a survey
G. Ditzler, M. Roveri, C. Alippi, and R. Polikar
IEEE Computational Intelligence Magazine, 2015, vol. 10, no. 4, pp. 12-25. [supplementary ]
A bootstrap based Neyman-Pearson test for identifying variable importance
G. Ditzler, R. Polikar, and G. Rosen
IEEE Transactions on Neural Networks and Learning Systems, 2015, vol. 26, no. 4, pp. 880-886.
[code ]
Incremental learning of concept drift from streaming imbalanced data
G. Ditzler and R. Polikar
in IEEE Transactions on Knowledge & Data Engineering, 2013, vol. 25, no. 10, pp. 2283–2301.
Conferences
Nonlinear Brain Tumor Model Estimation with Long Short-Term Memory Neural Networks
J. Guo, Z. Liang, E. Scribner, G. Ditzler, N. Bouaynaya and H. Fathallah-Shaykh
IEEE/INNS International Joint Conference on Neural Networks, 2018.
Fine Tuning Lasso in an Adversarial Environment Against Gradient Attacks
G. Ditzler and A. Prater
IEEE Symposium Series on Computational Intelligence, 2017.
Speeding Up Joint Mutual Information Feature Selection with an Optimization Heuristic
H. Liu and G. Ditzler
IEEE Symposium Series on Computational Intelligence, 2017.
Framework to Support DDDAS Decision” Support Systems: Design Overview
G. Ditzler, A. Akoglu, and S. Hariri
Workshop on InfoSymbiotics: DDDAS Dynamic Data Driven Applications Systems, 2017.
Autonomic Management of 3D Cardiac Simulations
E. Esmaili, A. Akoglu, G. Ditzler, S. Hariri, J. Szep and T. Moukabary
IEEE International Conference on Cloud and Autonomic Computing, 2017. (Best Paper Award)
A Self-Protection Agent using Error Correcting Output Codes to Secure Computers and Applications
F. de la Pena Montero, S. Hariri, and G. Ditzler
IEEE International Conference on Cloud and Autonomic Computing, 2017.
Fraud Analysis Approaches in the Age of Big Data - A Review of State of the Art
S. Makki, R. Haque, Y. Taher, Z. Assaghir, G. Ditzler, M.-S. Hacid and H. Zeineddine
International Workshop on Autonomic Systems for Big Data Analytics, 2017.
A Fast Information-theoretic Approximation of Joint Mutual Information Feature Selection
H. Liu and G. Ditzler
IEEE/INNS International Joint Conference on Neural Networks, 2017.
The AKRON-Kalman Filter for Tracking Time-Varying Networks
V. Carluccio, N. Bouaynaya, G. Ditzler, and H. M. Fathallah Shaykh
IEEE International Conference on Biomedical and Health Informatics, 2017.
A Study of Incremental Spectral Meta-Learning for Nonstationary Environments
G. Ditzler
IEEE/INNS International Joint Conference on Neural Networks, 2016.
Scaling a Neyman-Pearson subset selection approach via heuristics for mining massive data
G. Ditzler, M. Austen, R, Polikar, and G. Rosen
IEEE Symposium on Computational Intelligence in Data Mining, 2014. (travel award) [code ]
Feature Subset Selection for Inferring Relative Importance of Taxonomy
G. Ditzler and G. Rosen
ACM International Workshop on Big Data in Life Sciences, 2014. (invited and travel award)
Domain Adaptation Bounds for Multiple Expert Systems Under Concept Drift
G. Ditzler, G. Rosen, and R. Polikar
IEEE/INNS International Joint Conference on Neural Networks, 2014. (travel award & best student paper) [code ]
Incremental learning of new classes with unbalanced data
G. Ditzler, G. Rosen, and R. Polikar
IEEE/INNS International Joint Conference on Neural Networks, 2013.
Discounted expert weighting for concept drift
G. Ditzler, G. Rosen and R. Polikar
IEEE International Symposium on Computational Intelligence in Dynamic and Uncertain Environments, 2013.
Information theoretic feature selection for high dimensional metagenomic data
G. Ditzler, R. Polikar, and G. Rosen
in IEEE International Workshop on Genomic Signal Processing and Statistics, 2012.
A transductive learning algorithm for concept drift
G. Ditzler, G. Rosen and R. Polikar
in IEEE/INNS International Joint Conference on Neural Networks, 2012.
Determining significance in metagenomics
G. Ditzler, R. Polikar and G. Rosen,
in North Eastern Biomedical Engineering Conference, 2012.
Forensic identification with environmental samples
G. Ditzler, R. Polikar, and G. Rosen
in IEEE International Conference on Acoustic, Speech and Signal Processing, 2012.
Semi-supervised learning in nonstationary environments
G. Ditzler and R. Polikar
in IEEE/INNS International Joint Conference on Neural Networks, 2011. (student travel award)
Hellinger distance based drift detection algorithm
G. Ditzler and R. Polikar
in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, 2011.
Fusion methods for boosting performance of speaker identification systems
G. Ditzler, J. Ethridge, R. Polikar, and R. Ramachandranin
Asia Pacific Conference of Circuits and Systems, 2010.
An incremental learning algorithm for nonstationary environments and imbalanced data
G. Ditzler, R. Polikar, and N. V. Chawla
in International Conference on Pattern Recognition, 2010.
Optimal nu-SVM parameter estimation using multi-objective evolutionary algorithms
J. Ethridge, G. Ditzler, and R. Polikar
in IEEE Congress on Evolutionary Computing, 2010.
An ensemble-based incremental learning framework for concept drift and class imbalance
G. Ditzler and R. Polikar
in IEEE/INNS International Joint Conference on Neural Networks, 2010.
Incremental learning of new classes in unbalanced data: Learn++.UDNC
G. Ditzler, M. Muhlbaier, and R. Polikar in International Workshop on Multiple Classifier Systems, 2010.
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