Publications

You can also find me on Google Scholar. You can find many of the slides presented at various conferences on SpeakerDeck.

Journals

  1. G. Ditzler, R. Polikar, and G. Rosen, “A bootstrap based Neyman-Pearson test for identifying variable importance,” IEEE Transactions on Neural Networks and Learning Systems, vol. 26, no. 4, pp. 880-886, 2015. (link)
  2. G. Ditzler and R. Polikar,“Incremental learning of concept drift from streaming imbalanced data,” in IEEE Transactions on Knowledge & Data Engineering, 2013, vol. 25, no. 10, pp. 2283–2301. (link)

Book Chapters

  1. J.-L. Bouchot, W. Trimble, G. Ditzler, Y. Lan, S. Essinger, and G. Rosen, “Advances in machine learning for processing and comparison of metagenomic data,” Computational Systems Biology, Springer, 2013, To appear. (link)
  2. G. Ditzler, Y. Lan, J.-L. Bouchot, and G. Rosen, “Feature selection for metagenomic data analysis,” Encyclopedia of Metagenomics, 2014, To appear. (link)
  3. C. Alippi, G. Boracchi, G. Ditzler, R. Polikar, and M. Roveri, “Adaptive classifiers for nonstationary environments,” Contemporary Issues in Systems, Science and Engineering, IEEE/Wiley Press Book Series, to appear in 2013.

Conferences

  1. G. Ditzler, M. Austen, G. Rosen, and R. Polikar, “Scaling a Neyman-Pearson Subset Selection Approach Via Heuristics for Mining Massive Data,” in IEEE Symposium on Computational Intelligence and Data Mining, 2014. (link)
  2. G. Ditzler, G. Rosen, and R. Polikar, “Domain adaptation bounds for multiple expert systems under concept drift,” in International Joint Conference on Neural Networks, Beijing, China, 2014. (link)
  3. G. Ditzler, G. Rosen, and R. Polikar, “Incremental learning of new classes from unbalanced data,” in International Joint Conference on Neural Networks, Dallas, TX, 2013. (link)
  4. G. Ditzler, G. Rosen, and R. Polikar, “Discounted expert weighting for concept drift,” in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, Singapore, 2013, pp. 61–67. (link)
  5. G. Ditzler, R. Polikar, and G. Rosen, “Information theoretic feature selection for high dimensional metagenomic data,” in International Workshop on Genomic Signal Processing and Statistics, Washington, DC, 2012, pp. 143–146. (link)
  6. G. Ditzler, G. Rosen, and R. Polikar, “A transductive learning algorithm for nonstationary environments,” in International Joint Conference on Neural Networks, Brisbane, Australia, 2012, pp. 945–952. (link)
  7. G. Ditzler, R. Polikar, and G. Rosen, “Determining significance in metagenomic samples,” in Northeastern Bioengineering Conference, Philadelphia, USA, 2012, pp. 385–386. (link)
  8. G. Ditzler, R. Polikar, and G. Rosen, “Forensic identification with environmental samples,” in International Conference on Acoustics, Speech and Signal Processing, Kyoto, Japan, 2012, pp. 1861–1864. (link)
  9. G. Ditzler and R. Polikar, “Semi-supervised learning in nonstationary environments” in International Joint Conference on Neural Networks, 2011, San Jose, CA, pp. 2471–2478. (link)
  10. G. Ditzler and R. Polikar, “Hellinger distance based drift detection algorithm,” in IEEE Symposium on Computational Intelligence in Dynamic and Uncertain Environments, 2011, Paris, France, pp. 41–48. (link)
  11. G. Ditzler, J. Ethridge, R. Polikar, and R. Ramachandran, “Fusion methods for boosting performance of speaker identification systems,” in Asia Pacific Conference of Circuits and Systems, 2010, Kuala Lampur, Malaysia, pp. 116–119. (link)
  12. G. Ditzler, R.Polikar, and N. Chawla, “An incremental learning algorithm for nonstationary environments and imbalanced data,” International Conference on Pattern Recognition, 2010, Istanbul, Turkey, pp. 2997–3000. (link)
  13. J. Ethridge, G. Ditzler, and R. Polikar, “Optimal $\nu$-SVM parameter estimation using multi- objective evolutionary algorithms,” in IEEE Congress on Evolutionary Computing, 2010, Barcelona, Spain, pp. 3570–3577. (link)
  14. G. Ditzler and R. Polikar, “An incremental learning framework for concept drift and class imbalance.” in International Joint Conference on Neural Networks, 2010, Barcelona, Spain, pp. 736-743. (link)
  15. G.Ditzler, M. Muhlbaier, and R. Polikar, “Incremental learning of new classes in unbalanced data: Learn++.UDNC,” in International Workshop on Multiple Classifier Systems, 2010, Lecture Notes in Computer Science, N. El. Gayer et al, vol. 5997, Cairo, Egypt, pp. 33–42. (link)

Peer-Reviewed Workshops

  1. G. Ditzler and G. Rosen, “Feature subset selection for relative importance of taxonomy,” ACM International Workshop on Big Data in Life Sciences, 2014. (Invited) (link)

Abstracts & Posters

  1. G. Ditzler, J. Calvin Morrison, and G. Rosen, “FizzyQIIME: Feature Selection for Metagenomics,” Genomic Science Annual Contractor-Grantee Meeting/USDA-DOE Plant Feedstock Genomics for Bioenergy, 2014.
  2. J.-L. Bouchot, G. Ditzler, and G. Rosen, “The Earth Microbiome Project from a Data Science Perspective”, DTRA/NSF/NGA Algorithms Workshop, 2014.
  3. G. Ditzler, R. Polikar, and G. Rosen, “Application of a post-hoc Neyman-Pearson hypothesis test for identifying variable importance in comparative metagenomics,” DTRA/NSF/NGA Algorithms Workshop, 2014.
  4. G. Ditzler, Y. Lan, and G. Rosen, “Functional feature selection over varying phenotypes: Integration of feature selection methods into KBase,” Genomic Science Annual Contractor-Grantee Meeting, USDA/DOE Plant Feedstock Genomics for Bioenergy, 2013. poster & presentation.
  5. G. Ditzler and G. Rosen, “Deep learning of feature and structure for soil samples,” DTRA/NSF/NGA Algorithms for Threat Detection Workshop, 2012. poster.

Thesis

  1. G. Ditzler, “Incremental learning of concept drift from imbalanced data,” Masters thesis, Rowan University, 2011. (link)
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