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Missing Data Problems in Machine Learning
, 2008
"... Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. This thesis focuses on the problems of collaborative prediction with nonrandom missing data and classification with missing features. We begin by presenting ..."
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Cited by 7 (0 self)
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Learning, inference, and prediction in the presence of missing data are pervasive problems in machine learning and statistical data analysis. This thesis focuses on the problems of collaborative prediction with nonrandom missing data and classification with missing features. We begin by presenting
Iterated Importance Sampling in Missing Data Problems
, 2005
"... Missing variable models are typical benchmarks for new computational techniques in that the illposed nature of missing variable models o#er a challenging testing ground for these techniques. This was the case for the EM algorithm and the Gibbs sampler, and this is also true for importance sampling ..."
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Cited by 22 (7 self)
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schemes. A population Monte Carlo scheme taking advantage of the latent structure of the problem is proposed. The potential of this approach and its specifics in missing data problems are illustrated in settings of increasing di#culty, in comparison with existing approaches. The improvement brought by a
Application of autoregressive spectral analysis to missing data problems
 IEEE Trans. on Instrumentation and Measurement
, 2004
"... Abstract—Time series solutions for spectral analysis in missing data problems use reconstruction of the missing data, or a maximum likelihood approach that analyzes only the available measured data. Maximum likelihood estimation yields the most accurate spectra. An approximate maximum likelihood alg ..."
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Cited by 2 (1 self)
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Abstract—Time series solutions for spectral analysis in missing data problems use reconstruction of the missing data, or a maximum likelihood approach that analyzes only the available measured data. Maximum likelihood estimation yields the most accurate spectra. An approximate maximum likelihood
The Significance of the Missing Data Problem in Knowledge Discovery
, 1998
"... . Knowledge discovery in databases (KDD) is a field that is enjoying much attention in the literature and rapid growth in algorithms, techniques, and available software. KDD is defined as a nontrivial process which gleans valid, previously unknown, and potentially useful information from stored dat ..."
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consuming task. Yet, without proper preprocessing, the results of knowledge discovery are suspect. Data preprocessing includes data cleaning, data attribute selection, and dealing with missing attribute values. The problem of dealing with missing values is of significant interest to the KDD research community
Madagascar revisited: A missing data problem: SEP–97
, 1998
"... The Madagascar satellite data set provides images of a spreading ridge off the coast of Madagascar. This data set has two regions: the southern half is densely sampled and the northern half is sparsely sampled. The sparsely sampled region presents a missing data problem. I am using predictionerror ..."
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Cited by 4 (1 self)
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The Madagascar satellite data set provides images of a spreading ridge off the coast of Madagascar. This data set has two regions: the southern half is densely sampled and the northern half is sparsely sampled. The sparsely sampled region presents a missing data problem. I am using prediction
Asymmetric MissingData Problems: Overcoming the Lack of Negative Data in Preference Ranking
 INFORMATION RETRIEVAL
, 2002
"... In certain classification problems there is a strong asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples for t ..."
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Cited by 5 (1 self)
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In certain classification problems there is a strong asymmetry between the number of labeled examples available for each of the classes involved. In an extreme case, there may be a complete lack of labeled data for one of the classes while, at the same time, there are adequate labeled examples
Explaining Exceptional Cases Focusing on Solving the Missing Data Problem
"... Abstract. In medical practice and in knowledgebased systems too, it is necessary to consider exceptions and to deal with them appropriately. In this paper, a system is presented, which helps to explain cases that do not fit to a theoretical hypothesis. It is proposed to combine CaseBased Reasoning ..."
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Based Reasoning with a statistical model, where CaseBased Reasoning is used to explain the exceptional cases. Additionally, a method to partly solve the missing data problem was developed. This method combines general restoration techniques with domain dependent formulas provided by an expert. For the latter
Optimization Algorithms on Subspaces: Revisiting Missing Data Problem in LowRank Matrix
"... Abstract Lowrank matrix approximation has applications in many fields, such as 3D reconstruction from an image sequence and 2D filter design. In this paper, one issue with lowrank matrix approximation is reinvestigated: the missing data problem. Much effort was devoted to this problem, and the ..."
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Abstract Lowrank matrix approximation has applications in many fields, such as 3D reconstruction from an image sequence and 2D filter design. In this paper, one issue with lowrank matrix approximation is reinvestigated: the missing data problem. Much effort was devoted to this problem
INVERSE PROBABILITY WEIGHTED ESTIMATION FOR GENERAL MISSING DATA PROBLEMS
"... I study inverse probability weighted Mestimation under a general missing data scheme. Examples include Mestimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect in the linear exponential family, and variable probability sampling with ..."
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Cited by 37 (3 self)
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I study inverse probability weighted Mestimation under a general missing data scheme. Examples include Mestimation with missing data due to a censored survival time, propensity score estimation of the average treatment effect in the linear exponential family, and variable probability sampling
Results 1  10
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973,052