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- W861962728 abstract "In various fields as web mining, bioinformatics, statistical data analysis, and so forth, very diversely missing values are found. These values make training data to be sparse. Largely, the missing values are replaced by predicted values using mean and mode. We can used the advanced missing value imputation methods as conditional mean, tree method, and Markov Chain Monte Carlo algorithm. But general imputation models have the property that their predictive accuracy is decreased according to increase the ratio of missing in training data. Moreover the number of available imputations is limited by increasing missing ratio. To settle this problem, we proposed statistical learning theory to preprocess for missing values. Our statistical learning theory is the support vector regression by Vapnik. The proposed method can be applied to sparsely training data. We verified the performance of our model using the data sets from UCI machine learning repository." @default.
- W861962728 created "2016-06-24" @default.
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- W861962728 date "2004-10-01" @default.
- W861962728 modified "2023-10-14" @default.
- W861962728 title "A Sparse Data Preprocessing Using Support Vector Regression" @default.
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- W861962728 doi "https://doi.org/10.5391/jkiis.2004.14.6.789" @default.
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