Handling Missing Values when Applying Classification Models

A method for analyzing various components in a natural gas pipeline with the aid of a computer controlled gas chromatograph comprising the steps of: (a) providing the computer control unit with a data base for operating the gas chromatograph including at least: (1) periodically causing a sample of the natural gas to be supplied to the gas chromatograph; (2) operating the gas chromatograph to analyze the various components in the natural gas stream; (3) computing the amount of the various components in the natural gas stream; and (4) reporting the amount of components in the natural gas stream.

[1]  Zhiqiang Zheng,et al.  Personalization from incomplete data: what you don't know can hurt , 2001, KDD '01.

[2]  Gustavo E. A. P. A. Batista,et al.  An analysis of four missing data treatment methods for supervised learning , 2003, Appl. Artif. Intell..

[3]  Qiang Yang,et al.  Decision trees with minimal costs , 2004, ICML '04.

[4]  Avrim Blum,et al.  Combining labeled and unlabeled data with co-training , 1998, COLT' 98.

[5]  Jeffrey S. Simonoff,et al.  Tree Induction Vs Logistic Regression: A Learning Curve Analysis , 2003, J. Mach. Learn. Res..

[6]  G F Cooper,et al.  Algorithms for Bayesian belief-network precomputation. , 1991, Methods of information in medicine.

[7]  Dale Schuurmans,et al.  Learning to classify incomplete examples , 1997, COLT 1997.

[8]  Michael I. Jordan,et al.  Mixture models for learning from incomplete data , 1997, COLT 1997.

[9]  A. J. Feelders Handling Missing Data in Trees: Surrogate Splits or Statistical Imputation , 1999, PKDD.

[10]  Nicole A. Lazar,et al.  Statistical Analysis With Missing Data , 2003, Technometrics.

[11]  David E. Booth Analysis of Incomplete Multivariate Data , 2000, Technometrics.

[12]  Catherine Blake,et al.  UCI Repository of machine learning databases , 1998 .

[13]  Ray Bareiss,et al.  Concept Learning and Heuristic Classification in WeakTtheory Domains , 1990, Artif. Intell..

[14]  Eric R. Ziegel,et al.  The Elements of Statistical Learning , 2003, Technometrics.

[15]  Leslie G. Valiant,et al.  A theory of the learnable , 1984, STOC '84.

[16]  Kamal Nigam,et al.  Understanding the Behavior of Co-training , 2000, KDD 2000.

[17]  David Maxwell Chickering,et al.  Dependency Networks for Inference, Collaborative Filtering, and Data Visualization , 2001, J. Mach. Learn. Res..

[18]  Eibe Frank,et al.  Logistic Model Trees , 2005, Machine Learning.

[19]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[20]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artificial Intelligence.

[21]  Michael I. Jordan,et al.  Supervised learning from incomplete data via an EM approach , 1993, NIPS.

[22]  Eric Bauer,et al.  An Empirical Comparison of Voting Classification Algorithms: Bagging, Boosting, and Variants , 1999, Machine Learning.

[23]  Ron Kohavi,et al.  Lazy Decision Trees , 1996, AAAI/IAAI, Vol. 1.

[24]  Alexander Kogan,et al.  Knowing what doesn't Matter: Exploiting the Omission of Irrelevant Data , 1997, Artif. Intell..

[25]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[26]  Ian H. Witten,et al.  Data mining: practical machine learning tools and techniques with Java implementations , 2002, SGMD.

[27]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[28]  Leo Breiman,et al.  Bagging predictors , 2004, Machine Learning.

[29]  Nir Friedman,et al.  Learning Bayesian Networks with Local Structure , 1996, UAI.

[30]  Dale Schuurmans,et al.  Learning Bayesian Nets that Perform Well , 1997, UAI.

[31]  J. Ross Quinlan,et al.  Unknown Attribute Values in Induction , 1989, ML.

[32]  Rich Caruana,et al.  Predicting good probabilities with supervised learning , 2005, ICML '05.

[33]  Jennifer Neville,et al.  Relational Dependency Networks , 2007, J. Mach. Learn. Res..

[34]  Ben Taskar,et al.  Learning Probabilistic Models of Link Structure , 2002, J. Mach. Learn. Res..