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Hybrid imputation-based optimal evidential classification for missing data
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DOI：10.1007/s10489-024-05950-9.png)
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Abstract
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Classifying incomplete data remains a challenging task, as missing values can provide uncertain and imprecise information that reduces classification performance. To address this issue, we proposed a hybrid imputation-based optimal evidential classification (HOEC) method for missing data under the Dempster-Shafer theory framework. The proposed HOEC method can capture uncertainty and imprecision during imputation and classification procedures. Specifically, a hybrid imputation strategy was developed to estimate the missing values in the training and test sets by combining single and multiple imputations. Thus, we obtained accurate estimations and captured their uncertainties. An optimal evidential partition rule was then designed to adaptively submit an incomplete sample to a singleton class or meta-class under the Dempster-Shafer theory framework. Therefore, we can capture the imprecision caused by missing values and reduce classification errors. Experiments on several incomplete datasets demonstrated the effectiveness of the HOEC method compared with related methods.
Keywords:
Incomplete sample
Dempster-Shafer theory
Evidential classification
Missing data
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