Nearest neighbor imputation algorithms: a critical evaluation

By Lorenzo Beretta, Alessandro Santaniello. BMC Medical Informatics and Decision Making, 2016

627 citations recorded by the source corpus.

BackgroundNearest neighbor (NN) imputation algorithms are efficient methods to fill in missing data where each missing value on some records is replaced by a value obtained from related cases in the whole set of records. Besides the capability to substitute the missing data with plausible values that are as close as possible to the true value, imputation algorithms should preserve the original data structure and avoid to distort the distribution of the imputed variable. Despite the efficiency of NN algorithms little is known about the effect of these methods on data structure.MethodsSimulation on synthetic datasets with different patterns and degrees of missingness were conducted to evaluate the performance of NN with one single neighbor (1NN) and with k neighbors without (kNN) or with weighting (wkNN) in the context of different learning frameworks: plain set, reduced set after ReliefF filtering, bagging, random choice of attributes, bagging combined with random choice of attributes (Random-Forest-like method).ResultsWhatever the framework, kNN usually outperformed 1NN in terms of precision of imputation and reduced errors in inferential statistics, 1NN was however the only method capable of preserving the data structure and data were distorted even when small values of k neighbors were considered; distortion was more severe for resampling schemas.ConclusionsThe use of three neighbors in conjunction with ReliefF seems to provide the best trade-off between imputation error and preservation of the data structure. The very same conclusions can be drawn when imputation experiments were conducted on the single proton emission computed tomography (SPECTF) heart dataset after introduction of missing data completely at random.

Subfields: Artificial Intelligence, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/d006ccf2ccbd194a384aec6789eee3af937d819a

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Citations in the corpus

• A survey on missing data in machine learning (2021, methodology): “[66] 2016 Springer Nearest neighbor imputation algorithms: a critical evaluation BMC medical informatics and decision making [108] 2017 Springer Multiple imputation and ensemble learning for classification with incomplete data Intelligent and Evolutionary Systems [71] 2018 Springer NS-kNN: A modifie”