@misc{10498/34219,
year = {2021},
url = {http://hdl.handle.net/10498/34219},
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.},
publisher = {Springer},
keywords = {Data imputation},
keywords = {Missing value},
keywords = {Artificial neural network},
keywords = {Co-active neuro-fuzzy inference system},
title = {Co-active neuro-fuzzy inference system model as single imputation approach for non-monotone pattern of missing data},
doi = {10.1007/s00521-020-05661-5},
author = {Silva Ramírez, Esther Lydia and Cabrera Sánchez, Juan Francisco},
}