Missing value imputation through shorter interval selection driven by Fuzzy C-Means clustering

Khan, Hufsa; Wang, Xizhao; Liu, Han

doi:10.1016/j.compeleceng.2021.107230

Cited by 29 publications

(15 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All the other hyper-parameters are set to their default values. In [ 11 ], some learning algorithms, including © Bayes (NB), k-nearest neighbors (KNN), and support vector machine (SVM), were considered to have biases on some specific datasets or data. In addition, the decision tree (DT) method has good performance for multi-class tasks.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…In [ 11 , 33 ], the average classification accuracy obtained utilizing different imputation methods with different missing rates was selected to show an overall changing trend of continuous missing data. Figure 9 exhibits the average SVM classification accuracy based on different imputation models, where the x -axis and y -axis represent missing rates and average accuracy for each line chart, respectively.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…The selection of the classification learning algorithm always affects the final performance. Consequently, many studies aim to investigate the influence of the choice of different classifiers on imputation performance [ 11 , 40 , 51 ]. Figure 10 presents the integrated average accuracy for five missing proportions in the missing mechanism of MCAR.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…Compared with the statistics-based imputation methods, machine learning techniques make missing value imputation more precise and accurate by selecting the most similar patterns or features to the actual estimates of the missing values. Consequently, machine learning techniques play the same role as statistical techniques in reconstructing a complete dataset [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent

Pan

et al. 2022

Sensors

View full text Add to dashboard Cite

Data are a strategic resource for industrial production, and an efficient data-mining process will increase productivity. However, there exist many missing values in data collected in real life due to various problems. Because the missing data may reduce productivity, missing value imputation is an important research topic in data mining. At present, most studies mainly focus on imputation methods for continuous missing data, while a few concentrate on discrete missing data. In this paper, a discrete missing value imputation method based on a multilayer perceptron (MLP) is proposed, which employs a momentum gradient descent algorithm, and some prefilling strategies are utilized to improve the convergence speed of the MLP. To verify the effectiveness of the method, experiments are conducted to compare the classification accuracy with eight common imputation methods, such as the mode, random, hot-deck, KNN, autoencoder, and MLP, under different missing mechanisms and missing proportions. Experimental results verify that the improved MLP model (IMLP) can effectively impute discrete missing values in most situations under three missing patterns.

show abstract

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Experiments and Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent

Pan

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…In addition, clustering was used to analyze the commercial load profile [39] and also to analyze the behavior of renewable sources, just as in [40] where clustering was used for the coordination of wind turbines. In [41,42], wind power generation and quality were analyzed by using fuzzy c mean clustering [42]. In the existing research work related to DSM, most of them lack the modeling of hybrid energy resources [43].…”

Section: Introductionmentioning

confidence: 99%

Consumer-Driven Demand-Side Management Using K-Mean Clustering and Integer Programming in Standalone Renewable Grid

et al. 2022

Self Cite

View full text Add to dashboard Cite

Many countries have larger land areas and scattered communities. Therefore, to electrify them, small standalone power systems are the more preferred and cost-efficient solution as compared to utility grid extensions. The main objective of a standalone power system is to supply cleaner, cheaper, and uninterrupted electricity. However, for standalone power systems, demand-side management always remains a challenging task. In this paper, a load scheduling algorithm driven by K-mean clustering and linear integer programming to schedule consumers’ appliances for the upcoming day is proposed. In addition, the basic power to run the necessary appliances is kept available in the system all the time. Furthermore, to assist the consumer in every situation, the battery storage system and the overall system size reduction are also taken into consideration. Consumer input is also used in scheduling the appliances. The proposed method is evaluated on the publicly available real-world dataset; the simulation results demonstrate that the proposed approach performs better, due to which the reliability and continuity of the system are increased.

show abstract

Anomaly detection in smart grid using a trace-based graph deep learning model

Ida Evangeline,

Darwin,

Peter Anandkumar

et al. 2024

Electr Eng

View full text Add to dashboard Cite

Missing value imputation through shorter interval selection driven by Fuzzy C-Means clustering

Cited by 29 publications

References 35 publications

Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent

Discrete Missing Data Imputation Using Multilayer Perceptron and Momentum Gradient Descent

Consumer-Driven Demand-Side Management Using K-Mean Clustering and Integer Programming in Standalone Renewable Grid

Anomaly detection in smart grid using a trace-based graph deep learning model

Contact Info

Product

Resources

About