High-order polynomial interpolation with CNN: : A robust approach for missing data imputation
References
[1]
Khan H., Wang X., Liu H., Handling missing data through deep convolutional neural network, Inform Sci 595 (2022) 278–293.
[2]
Le Morvan M., Josse J., Scornet E., Varoquaux G., What’sa good imputation to predict with missing values?, Adv Neural Inf Process Syst 34 (2021) 11530–11540.
[3]
Khan H., Wang X., Liu H., A study on relationship between prediction uncertainty and robustness to noisy data, Int J Syst Sci 54 (6) (2023) 1243–1258.
[4]
Tirulo A., Chauhan S., Issac B., Ensemble LOF-based detection of false data injection in smart grid demand response system, Comput Electr Eng 116 (2024).
[5]
Rasheed M.T., Shi D., LSR: Lightening super-resolution deep network for low-light image enhancement, Neurocomputing 505 (2022) 263–275.
[6]
Rasheed M.T., Shi D., Khan H., A comprehensive experiment-based review of low-light image enhancement methods and benchmarking low-light image quality assessment, Signal Process (2022).
[7]
Rasheed M.T., Guo G., Shi D., Khan H., Cheng X., An empirical study on retinex methods for low-light image enhancement, Remote Sens 14 (18) (2022) 4608.
[8]
Pethuraj M.S., bin Mohd Aboobaider B., Salahuddin L.B., Developing lung cancer post-diagnosis system using pervasive data analytic framework, Comput Electr Eng 105 (2023).
[9]
Rao R.S., Kalabarige L.R., Alankar B., Sahu A.K., Multimodal imputation-based stacked ensemble for prediction and classification of air quality index in Indian cities, Comput Electr Eng 114 (2024).
[10]
Xie R., Jan N.M., Hao K., Chen L., Huang B., Supervised variational autoencoders for soft sensor modeling with missing data, IEEE Trans Ind Inf 16 (4) (2019) 2820–2828.
[11]
Basurto N., Arroyo Á., Cambra C., Herrero Á., Imputation of missing values affecting the software performance of component-based robots, Comput Electr Eng 87 (2020).
[12]
Nakagawa S., Freckleton R.P., Missing inaction: the dangers of ignoring missing data, Trends Ecol Evol 23 (11) (2008) 592–596.
[13]
Schafer J.L., Graham J.W., Missing data: our view of the state of the art, Psychol Methods 7 (2) (2002) 147.
[14]
Khan H., Wang X., Liu H., Missing value imputation through shorter interval selection driven by fuzzy C-means clustering, Comput Electr Eng 93 (2021).
[15]
Bokde N., Beck M.W., Álvarez F.M., Kulat K., A novel imputation methodology for time series based on pattern sequence forecasting, Pattern Recognit Lett 116 (2018) 88–96.
[16]
Cheng C.-H., Chan C.-P., Sheu Y.-J., A novel purity-based k nearest neighbors imputation method and its application in financial distress prediction, Eng Appl Artif Intell 81 (2019) 283–299.
[17]
Khan H., Liu H., Liu C., Missing label imputation through inception-based semi-supervised ensemble learning, Adv Comput Intell 2 (1) (2022) 10.
[18]
Purwar A., Singh S.K., Hybrid prediction model with missing value imputation for medical data, Expert Syst Appl 42 (13) (2015) 5621–5631.
[19]
Rasheed M.T., Shi D., Pixel-wise polynomial estimation model for low-light image enhancement, KSII Trans Internet Inf Syst (TIIS) 17 (9) (2023) 2483–2504.
[20]
Dua D., Graff C., UCI machine learning repository, 2017, URL http://archive.ics.uci.edu/ml.
[21]
Khan A., Rasheed M.T., Khan H., An empirical study of deep learning-based feature extractor models for imbalanced image classification, Adv Comput Intell 3 (6) (2023) 20.
[22]
Akande O., Li F., Reiter J., An empirical comparison of multiple imputation methods for categorical data, Amer Statist 71 (2) (2017) 162–170.
[23]
Li L., Prato C.G., Wang Y., Ranking contributors to traffic crashes on mountainous freeways from an incomplete dataset: A sequential approach of multivariate imputation by chained equations and random forest classifier, Accid Anal Prev 146 (2020).
[24]
Slade E., Naylor M.G., A fair comparison of tree-based and parametric methods in multiple imputation by chained equations, Stat Med 39 (8) (2020) 1156–1166.
[25]
Samad M.D., Abrar S., Diawara N., Missing value estimation using clustering and deep learning within multiple imputation framework, Knowl-Based Syst 249 (2022).
[26]
Garciarena U., Santana R., An extensive analysis of the interaction between missing data types, imputation methods, and supervised classifiers, Expert Syst Appl 89 (2017) 52–65.
[27]
Raja P., Thangavel K., Missing value imputation using unsupervised machine learning techniques, Soft Comput 24 (6) (2020) 4361–4392.
[28]
Figueroa-García J.C., Neruda R., Hernandez-Pérez G., A genetic algorithm for multivariate missing data imputation, Inform Sci 619 (2023) 947–967.
[29]
Karmitsa N., Taheri S., Bagirov A., Mäkinen P., Missing value imputation via clusterwise linear regression, IEEE Trans Knowl Data Eng 34 (4) (2020) 1889–1901.
[30]
Folguera L., Zupan J., Cicerone D., Magallanes J.F., Self-organizing maps for imputation of missing data in incomplete data matrices, Chemometr Intell Lab Syst 143 (2015) 146–151.
[31]
Nishanth K.J., Ravi V., Probabilistic neural network based categorical data imputation, Neurocomputing 218 (2016) 17–25.
[32]
Camino R.D., Hammerschmidt C.A., State R., Improving missing data imputation with deep generative models, 2019, arXiv preprint arXiv:1902.10666.
[33]
Zhang H., Xie P., Xing E., Missing value imputation based on deep generative models, 2018, arXiv preprint arXiv:1808.01684.
[34]
Zhuang Y., Ke R., Wang Y., Innovative method for traffic data imputation based on convolutional neural network, IET Intell Transp Syst 13 (4) (2019) 605–613.
[35]
Sangeetha M., Senthil Kumaran M., Deep learning-based data imputation on time-variant data using recurrent neural network, Soft Comput 24 (2020) 13369–13380.
[36]
Choudhury S.J., Pal N.R., Imputation of missing data with neural networks for classification, Knowl-Based Syst 182 (2019).
[37]
Lai X., Wu X., Zhang L., Lu W., Zhong C., Imputations of missing values using a tracking-removed autoencoder trained with incomplete data, Neurocomputing 366 (2019) 54–65.
[38]
Nazabal A., Olmos P.M., Ghahramani Z., Valera I., Handling incomplete heterogeneous data using vaes, Pattern Recognit 107 (2020).
[39]
Qiu Y.L., Zheng H., Gevaert O., Genomic data imputation with variational auto-encoders, GigaScience 9 (8) (2020) giaa082.
[40]
Pereira R.C., Abreu P.H., Rodrigues P.P., Partial multiple imputation with variational autoencoders: tackling not at randomness in healthcare data, IEEE J Biomed Health Inf 26 (8) (2022) 4218–4227.
[41]
Wang Y., Li D., Li X., Yang M., PC-GAIN: Pseudo-label conditional generative adversarial imputation networks for incomplete data, Neural Netw 141 (2021) 395–403.
[42]
Yuan Y., Zhang Y., Wang B., Peng Y., Hu Y., Yin B., STGAN: Spatio-temporal generative adversarial network for traffic data imputation, IEEE Trans Big Data 9 (1) (2022) 200–211.
[43]
Xia Y., Zhang L., Ravikumar N., Attar R., Piechnik S.K., Neubauer S., Petersen S.E., Frangi A.F., Recovering from missing data in population imaging–Cardiac MR image imputation via conditional generative adversarial nets, Med Image Anal 67 (2021).
[44]
Yoon J., Jordon J., Schaar M., Gain: Missing data imputation using generative adversarial nets, in: International conference on machine learning, PMLR, 2018, pp. 5689–5698.
[45]
Beretta L., Santaniello A., Nearest neighbor imputation algorithms: a critical evaluation, BMC Med Inform Decis Mak 16 (3) (2016) 197–208.
[46]
Stekhoven D.J., Bühlmann P., MissForest—non-parametric missing value imputation for mixed-type data, Bioinformatics 28 (1) (2012) 112–118.
[47]
Rubinsteyn A., Feldman S., fancyimpute: An imputation library for python, 2016, URL https://github.com/iskandr/fancyimpute.
[48]
Lall R., Robinson T., The MIDAS touch: accurate and scalable missing-data imputation with deep learning, Polit Anal 30 (2) (2022) 179–196.
Index Terms
- High-order polynomial interpolation with CNN: A robust approach for missing data imputation
Index terms have been assigned to the content through auto-classification.
Recommendations
Modified K-Nearest Neighbour Using Proposed Similarity Fuzzy Measure for Missing Data Imputation on Medical Datasets (MKNNMBI)
Early disease diagnosis is a burning problem in health sector, medical domain and disease management. During analysis, quality of the data can be achieved only if the data is complete. Missing values reduces the efficiency of data analysis task. ...
COLI: Collaborative clustering missing data imputation
Highlights- A novel collaborative clustering based imputation method is proposed.
- Imputation quality is used as information to be exchanged between the partitions.
- Results on experiments with different settings and specifications are ...
AbstractMissing data imputation plays an important role in the data cleansing process. Clustering algorithms have been widely used for missing data imputation, yet, there is little research done on the use of clustering ensemble for missing data ...
Deep learning versus conventional methods for missing data imputation: A review and comparative study
AbstractDeep learning models have been recently proposed in the applications of missing data imputation. In this paper, we review the popular statistical, machine learning, and deep learning approaches, and discuss the advantages and ...
Highlights- A comprehensive evaluation of deep learning models for missing data imputation.
Comments
Information & Contributors
Information
Published In
Elsevier Ltd.
Publisher
Pergamon Press, Inc.
United States
Publication History
Published: 01 October 2024
Author Tags
Qualifiers
- Research-article
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 0Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 12 Feb 2025