Hybrid Missing Value Imputation Algorithms Using Fuzzy C-Means and Vaguely Quantified Rough Set

Li, Daiwei; Zhang, Haiqing; Li, Tianrui; Bouras, Abdelaziz; Yu, Xi; Wang, Tao

doi:10.1109/tfuzz.2021.3058643

Cited by 31 publications

(13 citation statements)

References 45 publications

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“…Rani et al [62] proposed a hybrid imputation method to combine multivariate imputation by chained equations (MICE), K-nearest neighbor (KNN), mean and mode imputation methods for predicting missing values in medical datasets. Li et al [60] proposed hybrid missing value imputation algorithms JFCM-VQNNI and JFCM-FVQNNI that are utilized the combination of the fuzzy c-means and the vaguely quantified nearest neighbor.…”

Section: Background and Related Workmentioning

confidence: 99%

“…The SI and MI methods provide desirable results on MCAR and MAR patterns, respectively; besides, the constant global label is suitable for the MNAR missing data pattern [37,[55][56][57][58]. Accordingly, imputation of missing values via these methods might be introduced extra noises, biases, and poor data quality that provide less accuracy for the data model [59][60][61][62][63][64]. The presence of multipattern missing values can critically influence the performance of classifiers.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

et al. 2021

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Real medical datasets usually consist of missing data with different patterns which decrease the performance of classifiers used in intelligent healthcare and disease diagnosis systems. Many methods have been proposed to impute missing data, however, they do not fulfill the need for data quality especially in real datasets with different missing data patterns. In this paper, a four-layer model is introduced, and then a hybrid imputation (HIMP) method using this model is proposed to impute multi-pattern missing data including non-random, random, and completely random patterns. In HIMP, first, non-random missing data patterns are imputed, and then the obtained dataset is decomposed into two datasets containing random and completely random missing data patterns. Then, concerning the missing data patterns in each dataset, different single or multiple imputation methods are used. Finally, the best-imputed datasets gained from random and completely random patterns are merged to form the final dataset. The experimental evaluation was conducted by a real dataset named IRDia including all three missing data patterns. The proposed method and comparative methods were compared using different classifiers in terms of accuracy, precision, recall, and F1-score. The classifiers’ performances show that the HIMP can impute multi-pattern missing values more effectively than other comparative methods.

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Section: Background and Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

et al. 2021

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“…MVs are considered a critical problem that can occur in many scientific areas such as biological, psychological, or medical [1]. Commonly, many reasons may lead to the occurrence of MVs, for instance, wrong data entry, improper data collection, management of similar but not identical datasets and malfunctioning measurement equipment [2]. Machine learning (ML), big data and any data driven tool require high data quality which results in good analysis and outcomes.…”

Section: Introductionmentioning

confidence: 99%

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

Algarni¹,

Ragab²,

Alamri³

et al. 2022

Computer Systems Science and Engineering

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The accuracy of the statistical learning model depends on the learning technique used which in turn depends on the dataset's values. In most research studies, the existence of missing values (MVs) is a vital problem. In addition, any dataset with MVs cannot be used for further analysis or with any data driven tool especially when the percentage of MVs are high. In this paper, the authors propose a novel algorithm for dealing with MVs depending on the feature selection (FS) of similarity classifier with fuzzy entropy measure. The proposed algorithm imputes MVs in cumulative order. The candidate feature to be manipulated is selected using similarity classifier with Parkash's fuzzy entropy measure. The predictive model to predict MVs within the candidate feature is the Bayesian Ridge Regression (BRR) technique. Furthermore, any imputed features will be incorporated within the BRR equation to impute the MVs in the next chosen incomplete feature. The proposed algorithm was compared against some practical state-of-the-art imputation methods by conducting an experiment on four medical datasets which were gathered from several databases repository with MVs generated from the three missingness mechanisms. The evaluation metrics of mean absolute error (MAE), root mean square error (RMSE) and coefficient of determination (R 2 score) were used to measure the performance. The results exhibited that performance vary depending on the size of the dataset, amount of MVs and the missingness mechanism type. Moreover, compared to other methods, the results showed that the proposed method gives better accuracy and less error in most cases.

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“…Other approaches include alternating methods like k-subspaces [40], expectation-maximization [41], group-lasso [42], and lifting techniques [38], [43], [44] that require (at the very least) squaring the dimension of an already high-dimensional problem, which severely limits their applicability. More recently methods like [45], [46] incorporate a variation of fuzzy c-means for data imputation and/or clustering. However, these existing approaches either have limited applicability or do not perform well if data is missing in large quantities [50].…”

Section: Introductionmentioning

confidence: 99%

Fusion Subspace Clustering for Incomplete Data

Mahmood¹,

Pimentel-Alarcón²

2022

Preprint

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This paper introduces fusion subspace clustering, a novel method to learn low-dimensional structures that approximate large scale yet highly incomplete data. The main idea is to assign each datum to a subspace of its own, and minimize the distance between the subspaces of all data, so that subspaces of the same cluster get fused together. Our method allows low, high, and even full-rank data; it directly accounts for noise, and its sample complexity approaches the informationtheoretic limit. In addition, our approach provides a natural model selection clusterpath, and a direct completion method. We give convergence guarantees, analyze computational complexity, and show through extensive experiments on real and synthetic data that our approach performs comparably to the state-of-theart with complete data, and dramatically better if data is missing.

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Hybrid Missing Value Imputation Algorithms Using Fuzzy C-Means and Vaguely Quantified Rough Set

Cited by 31 publications

References 45 publications

A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

A Hybrid Imputation Method for Multi-Pattern Missing Data: A Case Study on Type II Diabetes Diagnosis

Towards Improving Predictive Statistical Learning Model Accuracy by Enhancing Learning Technique

Fusion Subspace Clustering for Incomplete Data

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