Bayesian networks for imputation in classification problems

Hruschka, Estevam R.; Hruschka, Eduardo R.; Ebecken, Nelson F. F.

doi:10.1007/s10844-006-0016-x

Cited by 55 publications

(20 citation statements)

References 24 publications

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“…Although they stress that MC and CMC are the less useful approaches, our results indicate that in case of K-NN the contrary is verified when analyzing more data sets. On the other hand, Hruschka et al [28] obtain that the best imputation method depends on the data set for C4.5, PART, and NB. However, due to the limited number of data sets used, these results are not as general as ours.…”

Section: Results For All the Classification Methodsmentioning

confidence: 99%

“…• Hruschka et al [28] propose two imputation methods based on Bayesian networks. They compare them with 4 classical imputation methods: EM, Data Augmentation, C4.5, and the CMC method, using 4 nominal data sets from the UCI repository [3] with natural MVs (but inducing MVs in them as well).…”

Section: An Overview Of the Analysis Of Imputation Methods In The Litmentioning

confidence: 99%

“…The literature on imputation methods in data mining employs well-known machine learning methods for their studies, in which the authors show the convenience of imputing the MVs for the mentioned algorithms, particularly for classification. The vast majority of MVs studies in classification usually analyze and compare one imputation method against a few others under controlled amounts of MVs and induce them artificially with known mechanisms and probability distributions [1,6,19,28,33].…”

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confidence: 99%

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On the choice of the best imputation methods for missing values considering three groups of classification methods

2011

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In real-life data, information is frequently lost in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on a classification task with twenty-three classification methods and fourteen different imputation approaches to missing values treatment that are presented and analyzed. The analysis involves a group-based approach, in which we distinguish between three different categories of classification methods. Each category behaves differently, and the evidence obtained shows that the use of determined missing values imputation methods could improve the accuracy obtained for these methods. In this study, the convenience of using imputation methods for preprocessing data sets with missing values is stated. The analysis suggests that the use of particular imputation methods conditioned to the groups is required.

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Section: Results For All the Classification Methodsmentioning

confidence: 99%

Section: An Overview Of the Analysis Of Imputation Methods In The Litmentioning

confidence: 99%

mentioning

confidence: 99%

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On the choice of the best imputation methods for missing values considering three groups of classification methods

2011

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“…Because the Bayesian network models the dependencies among all the features, it has proved to be effective in handling missing values in the dataset. 35,36 Bayesian statistical methods can smooth the model so that all available data can be used for training; thus, this process efficiently addresses the overfitting issue.…”

Section: Image Processing and Texture Analysismentioning

confidence: 99%

MRI Texture Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma

Dang

Lysack

et al. 2014

American Journal of Neuroradiology

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BACKGROUND AND PURPOSE:Head and neck cancer is common, and understanding the prognosis is an important part of patient management. In addition to the Tumor, Node, Metastasis staging system, tumor biomarkers are becoming more useful in understanding prognosis and directing treatment. We assessed whether MR imaging texture analysis would correctly classify oropharyngeal squamous cell carcinoma according to p53 status.

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“…The vast majority of MVs studies in classification usually analyze and compare one imputation method against a few others under controlled amounts of MVs, and induce them artificially with known mechanisms and probability distributions (Acuna and Rodriguez 2004;Batista and Monard 2003;Farhangfar et al 2008;Hruschka Jr. et al 2007;Li et al 2004;Luengo et al 2010).…”

Section: Introductionmentioning

confidence: 99%

Missing data imputation for fuzzy rule-based classification systems

2011

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Fuzzy rule-based classification systems (FRBCSs) are known due to their ability to treat with low quality data and obtain good results in these scenarios. However, their application in problems with missing data are uncommon while in real-life data, information is frequently incomplete in data mining, caused by the presence of missing values in attributes. Several schemes have been studied to overcome the drawbacks produced by missing values in data mining tasks; one of the most well known is based on preprocessing, formerly known as imputation. In this work, we focus on FRBCSs considering 14 different approaches to missing attribute values treatment that are presented and analyzed. The analysis involves three different methods, in which we distinguish between Mamdani and TSK models. From the obtained results, the convenience of using imputation methods for FRBCSs with missing values is stated. The analysis suggests that each type behaves differently while the use of determined missing values imputation methods could improve the accuracy obtained for these methods. Thus, the use of particular imputation methods conditioned to the type of FRBCSs is required.

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Bayesian networks for imputation in classification problems

Cited by 55 publications

References 24 publications

On the choice of the best imputation methods for missing values considering three groups of classification methods

On the choice of the best imputation methods for missing values considering three groups of classification methods

MRI Texture Analysis Predicts p53 Status in Head and Neck Squamous Cell Carcinoma

Missing data imputation for fuzzy rule-based classification systems

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