A Study of Data Imputation Using Fuzzy C-Means with Particle Swarm Optimization

Samat, Nurul Ashikin; Salleh, Mohd Najib Mohd

doi:10.1007/978-3-319-51281-5_10

Cited by 16 publications

(19 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The choice of parameters and depends on characteristics of the dataset and the relationship between each attributes. In recent years, intelligent optimization algorithm, such as PSO algorithm and genetic algorithm, is employed to optimize FCM parameters with a good performance [19,26]. Figure 4 is the flowchart of PSO-FCM-based imputation method.…”

Section: Pso-fcm-based Imputation Methodmentioning

confidence: 99%

“…The membership values of missing value "?" are estimated as (0.1, 0.3), (0.2, 0.5), and (0.7, 0.2), and the clustering centroids are estimated to be (120, 119), (87, 85), and (25,26). Therefore, if the abscissa value is missing, the missing value is calculated as "?"…”

Section: Fcm-based Imputationmentioning

confidence: 99%

“…In order to fully utilize the available spatial-temporal correlation data, fuzzy -means (FCM) is chosen as the basic algorithm, because of its excellent performance to analyze the data with multiple attributes, and the spatial-temporal correlation data can be expressed as multiple attributes. In addition, FCM not only has been successfully applied to address the clustering with incomplete data, but also has been applied to the estimation of missing data [23][24][25][26], including estimation of missing traffic volume data [19]. In this study, we draw on the principle of a SVR-based method for missing data imputation and taking into account the advantages of particle swarm optimization (PSO) algorithm [27] and then propose a new optimization algorithm that combines PSO and SVR to optimize the parameters of FCM.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

Shang

Yang

Gao

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

Missing traffic data are inevitable due to detector failure or communication failure. Currently, most of imputation methods estimated the missing traffic values by using spatial-temporal information as much as possible. However, it ignores an important fact that spatial-temporal information of the traffic missing data is often incomplete and unavailable. Moreover, most of the existing methods are verified by traffic data from freeway, and their applicability to urban road data needs to be further verified. In this paper, a hybrid method for missing traffic data imputation is proposed using FCM optimized by a combination of PSO algorithm and SVR. In this method, FCM is the basic algorithm and the parameters of FCM are optimized. Firstly, the patterns of missing traffic data are analyzed and the representation of missing traffic data is given using matrix-based data structure. Then, traffic data from urban expressway and urban arterial road are used to analyze spatial-temporal correlation of the traffic data for the determination of the proposed method input. Finally, numerical experiment is designed from three perspectives to test the performance of the proposed method. The experimental results demonstrate that the novel method not only has high imputation precision, but also exhibits good robustness.

show abstract

Section: Pso-fcm-based Imputation Methodmentioning

confidence: 99%

Section: Fcm-based Imputationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

Shang

Yang

Gao

et al. 2018

Journal of Advanced Transportation

View full text Add to dashboard Cite

show abstract

“…This model then evaluated by three measurements of performance which are MSE, MAE and MAPE by adjusted different degree of fitting (H) between 0 and 1 (see Table 5). The smallest error value is considered as the best FLR model for cluster 1 with H = 0.025 as in (17).…”

Section: Fuzzy Linear Regression (Zolfaghari) On Clustermentioning

confidence: 99%

“…Numerical method is used to identify the fuzzy regression model by minimising the sum of spreads of the estimated dependent variable. There are other quite considerable studies were carried out to use fuzzy and statistics techniques in Malaysia and other countries [17,18,19,20,21]. The fuzzy linear regression was focused on the FLR model with the assumption of triangular fuzzy numbers (TFNs) being either symmetrical or asymmetrical, where they both represents by its own membership function.…”

Section: Introductionmentioning

confidence: 99%

A new hybrid of Fuzzy C-Means Method and Fuzzy Linear Regression Model in Predicting Manufacturing Income

Ramly¹,

Rusiman²,

Him³

et al. 2018

IJET

View full text Add to dashboard Cite

Analysis by human perception could not be solved using traditional method since uncertainty within the data have to be dealt with first. Thus, fuzzy structure system is considered. The objectives of this study are to determine suitable cluster by using fuzzy c-means (FCM) method, to apply existing methods such as multiple linear regression (MLR) and fuzzy linear regression (FLR) as proposed by Tanaka and Ni and to improve the FCM method and FLR model proposed by Zolfaghari to predict manufacturing income. This study focused on FLR which is suitable for ambiguous data in modelling. Clustering is used to cluster or group the data according to its similarity where FCM is the best method. The performance of models will measure by using the mean square error (MSE), the mean absolute error (MAE) and the mean absolute percentage error (MAPE). Results shows that the improvisation of FCM method and FLR model obtained the lowest value of error measurement with MSE=1.825 , MAE=115932.702 and MAPE=95.0366. Therefore, as the conclusion, a new hybrid of FCM method and FLR model are the best model for predicting manufacturing income compared to the other models.

show abstract

A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data

et al. 2021

View full text Add to dashboard Cite

Incompleteness is one of the problematic data quality challenges in real-world machine learning tasks. A large number of studies have been conducted for addressing this challenge. However, most of the existing studies focus on the classification task and only a limited number of studies for symbolic regression with missing values exist . In this work, a new imputation method for symbolic regression with incomplete data is proposed. The method aims to improve both the effectiveness and efficiency of imputing missing values for symbolic regression. This method is based on genetic programming (GP) and weighted K-nearest neighbors (KNN). It constructs GP-based models using other available features to predict the missing values of incomplete features. The instances used for constructing such models are selected using weighted KNN. The experimental results on real-world data sets show that the proposed method outperforms a number of state-of-the-art methods with respect to the imputation accuracy, the symbolic regression performance, and the imputation time.

show abstract

A Study of Data Imputation Using Fuzzy C-Means with Particle Swarm Optimization

Cited by 16 publications

References 14 publications

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

An Imputation Method for Missing Traffic Data Based on FCM Optimized by PSO-SVR

A new hybrid of Fuzzy C-Means Method and Fuzzy Linear Regression Model in Predicting Manufacturing Income

A new imputation method based on genetic programming and weighted KNN for symbolic regression with incomplete data

Contact Info

Product

Resources

About