Under Review/Revision/In Preparation

  1. Speeding Up Joint Mutual Information Feature Selection with a Heuristic
    H. Liu and G. Ditzler
    submitted, 2017.
  2. Learning Variable Selection Models from Adversarial Environments
    G. Ditzler and A. Prater
    submitted, 2017.
  3. A Framework for Static Malicious HTML File Identification
    S. Hess, P. Satam, F. de la Pena Montero, S. Hariri, and G. Ditzler
    submitted, 2017.
  4. Extending Spectral Meta-Learning to Non-stationary Environments
    G. Ditzler
    in preparation (journal), 2017.
  5. Learning What We Don't Care About: Regularization with Sacrificial Functions
    G. Ditzler, M. Valenzuela, and J. Rozenblit
    in preparation (journal), 2017.
  6. Approximate kernel reconstructions for time-varying networks
    G. Ditzler, N. Bouaynaya, and H. M. Fathallah Shaykh
    in preparation (journal), 2017.

Book Chapters

  1. 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.
  2. Feature selection for metagenomic data analysis
    G. Ditzler, Y. Lan, J.-L. Bouchot, and G. Rosen
    Encyclopedia of Metagenomics, 2014.
  3. 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

  1. AKRON: An Algorithm for Approximating Sparse Kernel Reconstruction
    G. Ditzler, N. Bouaynaya, and R. Shterenberg
    Signal Processing, 2017. To appear.
  2. 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. To appear.
  3. 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. (accepted)
  4. Fizzy: Feature selection for metagenomics
    G. Ditzler, J. Calvin Morrison, Y. Lan, and G. Rosen
    BMC Bioinformatics, 2015, vol 16, no. 358. [code]
  5. 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.
  6. 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]
  7. 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]
  8. 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

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. A Study of Incremental Spectral Meta-Learning for Nonstationary Environments
    G. Ditzler
    IEEE/INNS International Joint Conference on Neural Networks, 2016.
  8. 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]
  9. 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)
  10. 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]
  11. Incremental learning of new classes with unbalanced data
    G. Ditzler, G. Rosen, and R. Polikar
    IEEE/INNS International Joint Conference on Neural Networks, 2013.
  12. 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.
  13. 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.
  14. A transductive learning algorithm for concept drift
    G. Ditzler, G. Rosen and R. Polikar
    in IEEE/INNS International Joint Conference on Neural Networks, 2012.
  15. Determining significance in metagenomics
    G. Ditzler, R. Polikar and G. Rosen,
    in North Eastern Biomedical Engineering Conference, 2012.
  16. Forensic identification with environmental samples
    G. Ditzler, R. Polikar, and G. Rosen
    in IEEE International Conference on Acoustic, Speech and Signal Processing, 2012.
  17. Semi-supervised learning in nonstationary environments
    G. Ditzler and R. Polikar
    in IEEE/INNS International Joint Conference on Neural Networks, 2011. (student travel award)
  18. Hellinger distance based drift detection algorithm
    G. Ditzler and R. Polikar
    in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, 2011.
  19. 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.
  20. 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.
  21. Optimal nu-SVM parameter estimation using multi-objective evolutionary algorithms
    J. Ethridge, G. Ditzler, and R. Polikar
    in IEEE Congress on Evolutionary Computing, 2010.
  22. 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.
  23. 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.