Artif Intell Med - A classifier ensemble approach for the missing feature problem.

Tópicos

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Resumo

JECTIVES: Many classification problems must deal with data that contains missing values. In such cases data imputation is critical. This paper evaluates the performance of several statistical and machine learning imputation methods, including our novel multiple imputation ensemble approach, using different datasets.MATERIALS AND METHODS: Several state-of-the-art approaches are compared using different datasets. Some state-of-the-art classifiers (including support vector machines and input decimated ensembles) are tested with several imputation methods. The novel approach proposed in this work is a multiple imputation method based on random subspace, where each missing value is calculated considering a different cluster of the data. We have used a fuzzy clustering approach for the clustering algorithm.RESULTS: Our experiments have shown that the proposed multiple imputation approach based on clustering and a random subspace classifier outperforms several other state-of-the-art approaches. Using the Wilcoxon signed-rank test (reject the null hypothesis, level of significance 0.05) we have shown that the proposed best approach is outperformed by the classifier trained using the original data (i.e., without missing values) only when >20% of the data are missed. Moreover, we have shown that coupling an imputation method with our cluster based imputation we outperform the base method (level of significance ~0.05).CONCLUSION: Starting from the assumptions that the feature set must be partially redundant and that the redundancy is distributed randomly over the feature set, we have proposed a method that works quite well even when a large percentage of the features is missing (=30%). Our best approach is available (MATLAB code) at bias.csr.unibo.it/nanni/MI.rar.

Resumo Limpo

jectiv mani classif problem must deal data contain miss valu case data imput critic paper evalu perform sever statist machin learn imput method includ novel multipl imput ensembl approach use differ datasetsmateri method sever stateoftheart approach compar use differ dataset stateoftheart classifi includ support vector machin input decim ensembl test sever imput method novel approach propos work multipl imput method base random subspac miss valu calcul consid differ cluster data use fuzzi cluster approach cluster algorithmresult experi shown propos multipl imput approach base cluster random subspac classifi outperform sever stateoftheart approach use wilcoxon signedrank test reject null hypothesi level signific shown propos best approach outperform classifi train use origin data ie without miss valu data miss moreov shown coupl imput method cluster base imput outperform base method level signific conclus start assumpt featur set must partial redund redund distribut random featur set propos method work quit well even larg percentag featur miss best approach avail matlab code biascsruniboitnannimirar

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