Impact of imputation of missing values on classification error for discrete data
نویسندگان
چکیده
Article history: Received 9 November 2006 Received in revised form 15 February 2008 Accepted 18 May 2008
منابع مشابه
Missing data imputation in multivariable time series data
Multivariate time series data are found in a variety of fields such as bioinformatics, biology, genetics, astronomy, geography and finance. Many time series datasets contain missing data. Multivariate time series missing data imputation is a challenging topic and needs to be carefully considered before learning or predicting time series. Frequent researches have been done on the use of diffe...
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Background and Objectives: Missing data exist in many studies, e.g. in regression models, and they decrease the model's efficacy. Many methods have been suggested for handling incomplete data: they have generally focused on missing outcome values. But covariate values can also be missing.Materials and Methods: In this paper we study the missing imputation by the EM algorithm and auxiliary varia...
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Background Policy makers need models to be able to detect groups at high risk of HIV infection. Incomplete records and dirty data are frequently seen in national data sets. Presence of missing data challenges the practice of model development. Several studies suggested that performance of imputation methods is acceptable when missing rate is moderate. One of the issues which was of less concern...
متن کاملEstimating Missing Values Using Mixture Kernel Regression
One of the important problem in data quality is the presence of missing data. So missing data imputation is an important issue in learning from incomplete data. Imputation is a procedure that replaces the missing values in a data set by some plausible values. Various techniques have been developed to deal with missing values in data sets with homogenous attributes. But those approaches are inde...
متن کاملچند رویکرد برخورد با مقادیر گمشده متغیرهای کمی و بررسی اثر آنها بر نتایج حاصل از یک کارآزمایی بالینی
Background and Objectives: A major challenge that affects the longitudinal studies is the problem of missing data. Missing in the data may result in the loss of part of the information which reduces the accuracy of the estimator and obtain the results will be biased and inaccurate. Therefore, it is necessary to evaluate the missing data mechanism from a longitudinal research and to consider thi...
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عنوان ژورنال:
- Pattern Recognition
دوره 41 شماره
صفحات -
تاریخ انتشار 2008