Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model

Sefidian, Amir Masoud; Daneshpour, Negin

doi:10.1016/j.eswa.2018.07.057

Cited by 91 publications

(58 citation statements)

References 53 publications

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“…Currently, with the aim to ensure that the estimated data are closer to the real data, other imputation methods that are based on the correlation between the missing data and the other existing data are being developed. In particular, these imputation methods include the regression imputation method [23][24][25][26], the k-nearest neighbor imputation (KNNI) method [27][28][29], the expectation maximization imputation (EMI) method [30], the knowledge-based method [31,32], and the fuzzy C-means method [33][34][35].…”

Section: Deletion Methodsmentioning

confidence: 99%

An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data

Huang

Mao

Bai

et al. 2020

Sensors

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Various traffic-sensing technologies have been employed to facilitate traffic control. Due to certain factors, e.g., malfunctioning devices and artificial mistakes, missing values typically occur in the Intelligent Transportation System (ITS) sensing datasets, resulting in a decrease in the data quality. In this study, an integrated imputation algorithm based on fuzzy C-means (FCM) and the genetic algorithm (GA) is proposed to improve the accuracy of the estimated values. The GA is applied to optimize the parameter of the membership degree and the number of cluster centroids in the FCM model. An experimental test of the taxi global positioning system (GPS) data in Manhattan, New York City, is employed to demonstrate the effectiveness of the integrated imputation approach. Three evaluation criteria, the root mean squared error (RMSE), correlation coefficient (R), and relative accuracy (RA), are used to verify the experimental results. Under the ±5% and ±10% thresholds, the average RAs obtained by the integrated imputation method are 0.576 and 0.785, which remain the highest among different methods, indicating that the integrated imputation method outperforms the history imputation method and the conventional FCM method. On the other hand, the clustering imputation performance with the Euclidean distance is better than that with the Manhattan distance. Thus, our proposed integrated imputation method can be employed to estimate the missing values in the daily traffic management.

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Section: Deletion Methodsmentioning

confidence: 99%

An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data

Huang

Mao

Bai

et al. 2020

Sensors

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“…A new hybrid technique based on a fuzzy c-means clustering algorithm, mutual information feature selection and regression models (GFCM) was developed by Sefidian and Daneshpour [61]. The aim was to find a set of similar records with high dependencies for a missing record and then apply regression imputation techniques within the group to estimate missing values for that record.…”

Section: Related Workmentioning

confidence: 99%

“…Since the accuracy of most machine learning algorithms for classification, regression and clustering could be affected by the completeness of datasets, processing and dealing with missing data is a significant step in data mining and machine learning processes. Yet, this is still underexplored in the literature [11,28,49,61,[68][69][70].…”

Section: Introductionmentioning

confidence: 99%

Multiple Imputation Ensembles (MIE) for Dealing with Missing Data

2020

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Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases.

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“…Although the fuzzy linguistic value was used to estimate the weights of the criteria, GRA was used in ranking the criteria for the evaluation of the supply chain. Sefidian and Daneshpour [56] developed the missing values imputation method that replaces the Euclidean distance with the GRA in the fuzzy clustering algorithm. Ramesh et al [57] applied GRA in combination with the Taguchi based orthogonal array in ranking the control factors with regards to the output of the design of experiments (DoE) approach, thereby obtaining the setting for a better cutting force.…”

Section: Grey System Theorymentioning

confidence: 99%

Grey Regulatory Focus Theory Weighting Method for the Multi-Criteria Decision-Making Problem in Evaluating University Reputation

Esangbedo

Bai

2019

Symmetry

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Evaluating the reputation of higher institutions is important as a benchmark for the continuous development of universities. In multi-criteria decision-making (MCDM), the estimation of the criteria weight is significant in improving the accuracy of the evaluation results. However, criteria weights assigned by decision makers (DMs) can be inaccurate when not measured from the orientations from which the DMs pursue their decision goals. This paper combines the grey system theory (GST) and regulatory focus theory (RFT) in estimating the weights of criteria by measuring the promotional and prevention focus orientations of the DMs and representing it as grey numbers. In this paper, we showed not all weight measurements from the DMs are symmetrical. In essence, to improve the accuracy of the MCDM weights, the grey regulatory focus theory (GRFT) weighting method is proposed to estimate the weights of an MCDM problem as well as aggregating the weights of the group DMs, thereby overcoming the limitation of a single orientation measurement. Furthermore, the GRFT weighting method is applied in ranking four universities in Xi’an city of China from a sample data from 1,200 students. The most reputable university is consistent with the other ranking indexes in the literature.

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Missing value imputation using a novel grey based fuzzy c-means, mutual information based feature selection, and regression model

Cited by 91 publications

References 53 publications

An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data

An Integrated Fuzzy C-Means Method for Missing Data Imputation Using Taxi GPS Data

Multiple Imputation Ensembles (MIE) for Dealing with Missing Data

Grey Regulatory Focus Theory Weighting Method for the Multi-Criteria Decision-Making Problem in Evaluating University Reputation

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