Pre-Processing of High-Dimensional Categorical Predictors in Classification Settings. - Eugene Tuv; George C. Runger (Appl. Artif. Intell., 2003)

Scoring Levels of Categorical Variables with Heterogeneous Data. - Eugene Tuv; George C. Runger (IEEE Intell. Syst., 2004)

Ensemble Learning with Supervised Kernels. - Kari Torkkola; Eugene Tuv (ECML, 2005)

Contributors to a signal from an artificial contrast. - Jing Hu; George C. Runger; Eugene Tuv (ICINCO, 2005)

Evolving Feature Selection. - Huan Liu 001; Edward R. Dougherty; Jennifer G. Dy; Kari Torkkola; Eugene Tuv; Hanchuan Peng; Chris H. Q. Ding; Fuhui Long; Michael E. Berens; Lance Parsons; Zheng Zhao 002; Lei Yu 001; et al. (IEEE Intell. Syst., 2005)

Tree-Based Ensembles with Dynamic Soft Feature Selection. - Alexander Borisov; Victor Eruhimov; Eugene Tuv (Feature Extraction, 2006)

Ensembles of Regularized Least Squares Classifiers for High-Dimensional Problems. - Kari Torkkola; Eugene Tuv (Feature Extraction, 2006)

Ensemble Learning. - Eugene Tuv (Feature Extraction, 2006)

Supervised compression of multivariate time series data. - Victor Eruhimov; Vladimir Martyanov; Peter Raulefs; Eugene Tuv (EUSIPCO, 2006)

Combining Unsupervised and Supervised Approaches to Feature Selection for Multivariate Signal Compression. - Victor Eruhimov; Vladimir Martyanov; Peter Raulefs; Eugene Tuv (IDEAL, 2006)

Best Subset Feature Selection for Massive Mixed-Type Problems. - Eugene Tuv; Alexander Borisov; Kari Torkkola (IDEAL, 2006)

Feature Selection Using Ensemble Based Ranking Against Artificial Contrasts. - Eugene Tuv; Alexander Borisov; Kari Torkkola (IJCNN, 2006)

Change-point detection with supervised learning and feature selection. - Victor Eruhimov; Vladimir Martyanov; Eugene Tuv; George C. Runger (ICINCO-ICSO, 2007)

Constructing High Dimensional Feature Space for Time Series Classification. - Victor Eruhimov; Vladimir Martyanov; Eugene Tuv (PKDD, 2007)

Ensembles of Least Squares Classifiers with Randomized Kernels. - Kari Torkkola; Eugene Tuv (Data Mining: Foundations and Practice, 2008)

Contributor Diagnostics for Anomaly Detection. - Alexander Borisov; George C. Runger; Eugene Tuv (ICANN (2), 2009)

Zero-Inflated Boosted Ensembles for Rare Event Counts. - Alexander Borisov; George C. Runger; Eugene Tuv; Nuttha Lurponglukana-Strand (IDA, 2009)

Feature Selection with Ensembles, Artificial Variables, and Redundancy Elimination. - Eugene Tuv; Alexander Borisov; George C. Runger; Kari Torkkola (J. Mach. Learn. Res., 2009)

High-Dimensional Surveillance. - Saylisse Dávila; George C. Runger; Eugene Tuv (ICANN (2), 2011)

Bias of Importance Measures for Multi-valued Attributes and Solutions. - Houtao Deng; George C. Runger; Eugene Tuv (ICANN (2), 2011)

Target-aware Anomaly Detection and Diagnosis. - Alexander Borisov; George C. Runger; Eugene Tuv (ICINCO (1), 2011)

Robust, Non-Redundant Feature Selection for Yield Analysis in Semiconductor Manufacturing. - Éric St.-Pierre; Eugene Tuv (ICDM, 2011)

Active Batch Learning with Tree Ensembles. - Alexander Borisov; Eugene Tuv; George C. Runger (Active Learning and Experimental Design @ AISTATS, 2011)

Learning Markov Blankets for Continuous or Discrete Networks via Feature Selection. - Houtao Deng; Saylisse Dávila; George C. Runger; Eugene Tuv (Ensembles in Machine Learning Applications, 2011)

A time series forest for classification and feature extraction. - Houtao Deng; George C. Runger; Eugene Tuv; Vladimir Martyanov (Inf. Sci., 2013)

A Bag-of-Features Framework to Classify Time Series. - Mustafa Gökçe Baydogan; George C. Runger; Eugene Tuv (IEEE Trans. Pattern Anal. Mach. Intell., 2013)

CBC: An associative classifier with a small number of rules. - Houtao Deng; George C. Runger; Eugene Tuv; Wade Bannister (Decis. Support Syst., 2014)