An Efficient and Effective Model to Handle Missing Data in Classification.
Missing data is one of the most important causes in reduction of classification accuracy. Many real datasets suffer from missing values, especially in medical sciences. Imputation is a common way to deal with incomplete datasets. There are various imputation methods that can be applied, and the choi...
| Published in: | BioMed Research International pp. 1 - 12 |
|---|---|
| Main Authors: | , , |
| Format: | research tables/charts Journal Article |
| Published: |
Wiley-Blackwell
11/25/2020
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=147200759&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 147200759 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/25/2020 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 147200759 147200759 147200759 10.1155/2020/8810143 147200759 ppf: 1 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: An Efficient and Effective Model to Handle Missing Data in Classification. aug: au: Mehrabani-Zeinabad, Kamran Doostfatemeh, Marziyeh Ayatollahi, Seyyed Mohammad Taghi affil: Department of Biostatistics, Faculty of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran sug: subj: Data Management Classification Human Regression Models, Statistical Decision Trees ab: Missing data is one of the most important causes in reduction of classification accuracy. Many real datasets suffer from missing values, especially in medical sciences. Imputation is a common way to deal with incomplete datasets. There are various imputation methods that can be applied, and the choice of the best method depends on the dataset conditions such as sample size, missing percent, and missing mechanism. Therefore, the better solution is to classify incomplete datasets without imputation and without any loss of information. The structure of the "Bayesian additive regression trees" (BART) model is improved with the "Missingness Incorporated in Attributes" approach to solve its inefficiency in handling the missingness problem. Implementation of MIA-within-BART is named "BART.m". As the abilities of BART.m are not investigated in classification of incomplete datasets, this simulation-based study aimed to provide such resource. The results indicate that BART.m can be used even for datasets with 90 missing present and more importantly, it diagnoses the irrelevant variables and removes them by its own. BART.m outperforms common models for classification with incomplete data, according to accuracy and computational time. Based on the revealed properties, it can be said that BART.m is a high accuracy model in classification of incomplete datasets which avoids any assumptions and preprocess steps. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|