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Iterative Robust Semi-Supervised Missing Data Imputation

Nikos Fazakis, Georgios Kostopoulos, Sotiris Kotsiantis, Iosif Mporas
2020 IEEE Access  
Semi-supervised methods have proved to be particularly effective for exploiting incomplete or partially labeled data with regard to the values of the target attribute.  ...  Imputation is often employed to overcome the shortcomings incurred by missing data during the pre-process stage of data analysis.  ...  The proposed algorithm is established on the basis of the Iterative Robust Model-based Imputation (IRMI) [21] algorithm.  ... 
doi:10.1109/access.2020.2994033 fatcat:w34k4rxwwrchfaadtrj4yxv3cu

Improving accuracy of missing data imputation in data mining

Nzar A. Ali, Zhyan M. Omer
2017 Kurdistan Journal of Applied Research  
PA) used as a complete value for imputing next missing value.We compare our proposed algorithm with several other algorithms such as MMS, HDI, KNNMI, FCMOCS, CRI, CMI, NIIA and MIGEC under different missing  ...  Missing data is a common drawback in many real-world data sets. In this paper, we proposed an algorithm depending on improving (MIGEC) algorithm in the way of imputation for dealing missing values.  ...  The statistical model for missing data is P (R\Y, Ø) where Ø is the parameter for the missing data process.  ... 
doi:10.24017/science.2017.3.30 fatcat:j4sf7uwntjd25j3z7i2sk2rq5e

An Efficient Missing Data Imputation Based On Co-Cluster Sparse Matrix Learning

F. Femila, G. Sridevi, D. Swathi, K. Swetha
2019 International Journal of Scientific Research in Computer Science Engineering and Information Technology  
This algorithm learns without reference class, and even with data continuous missing rate as high as the existing techniques.  ...  Missing data padding is an important problem that is faced in real time. This makes the task of data processing challenging.  ...  process datasets with missing data.  ... 
doi:10.32628/cseit195220 fatcat:rsi4z5kh4ncbvmeyofl7j37cre

Probability-based Imputation Method for Fuzzy Cluster Analysis of Gene Expression Microarray Data

Thanh Le, Tom Altman, Katheleen J. Gardiner
2012 2012 Ninth International Conference on Information Technology - New Generations  
We show that our method outperforms six popular imputation algorithms on uniform and nonuniform artificial datasets as well as real datasets with unknown data distribution model.  ...  To address this problem, we present a new algorithm where genes are clustered using the Fuzzy C-Means algorithm, followed by approximating the fuzzy partition by a probabilistic data distribution model  ...  .), and the Linda Crnic Institute for Down Syndrome and the National Institutes of Health HD056235 (to K.G.).  ... 
doi:10.1109/itng.2012.159 dblp:conf/itng/LeAG12 fatcat:7gjnecov25dhbm7nwir5lqtj2q

WebPut: Efficient Web-Based Data Imputation [chapter]

Zhixu Li, Mohamed A. Sharaf, Laurianne Sitbon, Shazia Sadiq, Marta Indulska, Xiaofang Zhou
2012 Lecture Notes in Computer Science  
Acknowledgments: The authors would like to thank the anonymous reviewers for their valuable comments and suggestions. This work was supported in part by Australian Research Council grant DP110102777.  ...  For an incomplete tuple, all identified data imputation queries are processed in a particular order so they form a greedy schedule for each incomplete tuple.  ...  Another line of data imputation approaches attempts to predict an estimation for the missing values using models built on the incomplete data set [15, 18, 21] .  ... 
doi:10.1007/978-3-642-35063-4_18 fatcat:ahdogneogja5lnqmatrz4d56pi

Takagi-Sugeno Modeling of Incomplete Data for Missing Value Imputation with the Use of Alternate Learning

Xiaochen Lai, Liyong Zhang, Xin Liu
2020 IEEE Access  
variables to drive the advance of incomplete data modeling and updates imputations with the adjustment of model parameters.  ...  In this paper, a method of Takagi-Sugeno (TS) fuzzy modeling for incomplete data is proposed and utilized to estimate missing values.  ...  Hence, in the process of incomplete data modeling, the way to handle the incomplete model input deserves great attention.  ... 
doi:10.1109/access.2020.2991669 fatcat:fht4qh3owrhldcty2mex2m6t7y

PCA model building with missing data: New proposals and a comparative study

A. Folch-Fortuny, F. Arteaga, A. Ferrer
2015 Chemometrics and Intelligent Laboratory Systems  
This paper introduces new methods for building principal component analysis (PCA) models with missing data: projection to the model plane (PMP), known data regression (KDR), KDR with principal component  ...  A comparative study is carried out comparing these new methods with the standard ones, such as the modified nonlinear iterative partial least squares (NIPALS), the iterative algorithm (IA), the data augmentation  ...  A common procedure for building a PCA model from X is the known iterative algorithm (IA) [5] that consists of filling in the missing data with initial values (usually zeros, although other imputations  ... 
doi:10.1016/j.chemolab.2015.05.006 fatcat:oju32f7ubvaaxnv2xmktuakl5u

Semi-supervised Learning with Missing Values Imputation [article]

Buliao Huang and Yunhui Zhu and Muhammad Usman and Huanhuan Chen
2021 arXiv   pre-print
Moreover, SSCFlow treats the initialized missing values as corrupted initial imputation and iteratively reconstructs their latent representations with an overcomplete denoising autoencoder to approximate  ...  However, this process often separates the imputation and classification, which may lead to inferior performance since label information are often ignored during imputation.  ...  Iterative Denoising Mechanism Since normalizing flow is a generative model, data imputation can be performed by sampling from the underlying data distributions learned by the model.  ... 
arXiv:2106.01708v2 fatcat:4fr74ttuljgmxiexjrva2too7e

Missing Values Imputation Based on Iterative Learning

Huaxiong Li
2013 International Journal of Intelligence Science  
In this paper, several methods for dealing with missing values in incomplete data are reviewed, and a new method for missing values imputation based on iterative learning is proposed.  ...  The iterative learning process will go on until an incomplete data is entirely converted to a complete data.  ...  Conclusion In this paper, several methods for dealing with missing values in incomplete data are reviewed, and a new method for missing values imputation based on iterative learning is proposed.  ... 
doi:10.4236/ijis.2013.31a006 fatcat:gqnmos6an5h73kj2jjkigu3lx4

Analysis of repeated categorical data using generalized estimating equations

Stuart R. Lipsitz, Kyungmann Kim, Lueping Zhao
1994 Statistics in Medicine  
Imputation mechanisms (M = 20): PROC MI with -MCMC statement for use of the data augmentation process -MONOTONE LOGISTIC statement for application of the ordinal imputation regression model 3.  ...  Objective: Compare the performance of the data augmentation (MCMC) and an ordinal imputation regression model (OIM) for incomplete longitudinal ordinal data for situations frequently encountered in practice  ... 
doi:10.1002/sim.4780131106 pmid:8091041 fatcat:45emkq2v2nfi3cbris32rdha4q

Attribute-Associated Neuron Modeling and Missing Value Imputation for Incomplete Data

Xiaochen Lai, Jinchong Zhu, Liyong Zhang, Zheng Zhang, Wei Lu, Nawab Muhammad Faseeh Qureshi
2021 Wireless Communications and Mobile Computing  
Besides, for the problem of incomplete model input, this paper proposes a model training scheme, which sets missing values as variables and makes missing value variables update with model parameters iteratively  ...  Based on the auto associative neural network (AANN), this paper conducts regression modeling for incomplete data and imputes missing values.  ...  data updated iteratively.  ... 
doi:10.1155/2021/5589872 fatcat:lnbvmsac5nfc3oceubbkbgcy4u

Clustering-Based Multiple Imputation via Gray Relational Analysis for Missing Data and Its Application to Aerospace Field

Jing Tian, Bing Yu, Dan Yu, Shilong Ma
2013 The Scientific World Journal  
Then, it utilizes the entropy of the proximal category for each incomplete instance in terms of the similarity metric based on gray relational analysis.  ...  Experiments on UCI datasets and aerospace datasets demonstrate that the superiority of our algorithm to other approaches on validity.  ...  State-of-the-Art for Missing Data Imputation. Statistical analysis with missing data has been noted in the literature for more than 70 years.  ... 
doi:10.1155/2013/720392 pmid:23737724 pmcid:PMC3659482 fatcat:xwhg5d4kf5cz5hpgfxjxn2hbwy

Incomplete Time Series Prediction Using Max-Margin Classification of Data with Absent Features

Shang Zhaowei, Zhang Lingfeng, Ma Shangjun, Fang Bin, Zhang Taiping
2010 Mathematical Problems in Engineering  
Compared with traditional predicting process of incomplete time series, our method solves the problem directly rather than fills the missing data in advance.  ...  The issue of modeling incomplete time series is considered as classification of data with absent features. We employ the optimal hyperplane of classification to predict the future values.  ...  The authors would like to thank the anonymous reviewers in MPE for helpful suggestions and corrections.  ... 
doi:10.1155/2010/513810 fatcat:mpxzpad2drhdvgqihmlvosqekq

Causal Discovery from Incomplete Data: A Deep Learning Approach [article]

Yuhao Wang, Vlado Menkovski, Hao Wang, Xin Du, Mykola Pechenizkiy
2020 arXiv   pre-print
To alleviate this issue, we proposed a deep learning framework, dubbed Imputated Causal Learning (ICL), to perform iterative missing data imputation and causal structure discovery.  ...  However, missing data are ubiquitous in practical scenarios. Directly performing existing casual discovery algorithms on partially observed data may lead to the incorrect inference.  ...  Concretely, in each iteration of our algorithm, G and D take the incomplete data as input and impute the missing values to formX. The causal structure B is involved as parameters of both SE and SD.  ... 
arXiv:2001.05343v1 fatcat:ugbb6dbvprgyvfoazaokwtjtwq

Advanced methods for missing values imputation based on similarity learning

Khaled M. Fouad, Mahmoud M. Ismail, Ahmad Taher Azar, Mona M. Arafa
2021 PeerJ Computer Science  
It integrates fuzzy c-means, k-nearest neighbors, and iterative imputation algorithms to impute the missing data in a dataset.  ...  A hybrid missing data imputation method is initially proposed, called KI, that incorporates k-nearest neighbors and iterative imputation algorithms.  ...  In order to reduce the computational time, the imputation process for the FCKI algorithm can be parallelized and we will also try to replace kNN with another method such as support vector machine (SVM)  ... 
doi:10.7717/peerj-cs.619 fatcat:2565kwessjerbgrhgxxfmpu2py
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