COMPARATIVE STUDY
JOURNAL ARTICLE
Add like
Add dislike
Add to saved papers

A classifier ensemble approach for the missing feature problem.

OBJECTIVES: 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.

Full text links

We have located links that may give you full text access.
Can't access the paper?
Try logging in through your university/institutional subscription. For a smoother one-click institutional access experience, please use our mobile app.

For the best experience, use the Read mobile app

Mobile app image

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app

All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.

By using this service, you agree to our terms of use and privacy policy.

Your Privacy Choices Toggle icon

You can now claim free CME credits for this literature searchClaim now

Get seemless 1-tap access through your institution/university

For the best experience, use the Read mobile app