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Co-active neuro-fuzzy inference system model as single imputation approach for non-monotone pattern of missing data

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A Correction to this article was published on 29 October 2021

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Abstract

Data imputation aims to solve missing values problem which is common in nowadays applications. Many techniques have been proposed to solve this problem from statistical methods such as Mean/Mode to machine learning models. In this paper, an approach based on Co-active Neuro-Fuzzy Inference System named CANFIS-ART is proposed to automate data imputation procedure. This model is constructed from the Neural Network adaptative capabilities and fuzzy logic qualitative approach using the Fuzzy-ART algorithm. Performance of CANFIS-ART model is compared to other state-of-the-art imputation techniques such as Multilayer Perceptron or Hot-Deck, among others, using a total of eighteen databases exposed to a perturbation procedure based on the random generation of non-monotone missing values pattern. The data sets cover a wide range of fields, types of variables and sizes. A comparison of databases imputed by these models using a set of three classifiers has been conducted. A statistical analysis of these results employing Wilcoxon signed-ranked test has been included. Experiments show that CANFIS-ART approach not only outperforms these state-of-the-art techniques but also demonstrates a higher level of generalization capability, increasing the data quality contained in databases with missing values.

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Acknowledgements

The authors would like to thank the editors and the reviewers for their valuable comments and suggestions, which improved this research. References Aquino et al. [3], Rubio [37], Meda [32], Rubio et al. [38] were added upon request by Reviewer 3.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, University of Cádiz, Avda. Universidad de Cádiz 10, 11519, Puerto Real, Cádiz, Spain

    Esther-Lydia Silva-Ramírez & Juan-Francisco Cabrera-Sánchez

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  1. Esther-Lydia Silva-Ramírez
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  2. Juan-Francisco Cabrera-Sánchez
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Correspondence to Esther-Lydia Silva-Ramírez.

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Silva-Ramírez, EL., Cabrera-Sánchez, JF. Co-active neuro-fuzzy inference system model as single imputation approach for non-monotone pattern of missing data. Neural Comput & Applic 33, 8981–9004 (2021). https://doi.org/10.1007/s00521-020-05661-5

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