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- research-articleMay 2024
Robust multipe imputation with GAM
Statistics and Computing (KLU-STCO), Volume 34, Issue 4Aug 2024https://doi.org/10.1007/s11222-024-10429-1AbstractMultiple imputation of missing values is a key step in data analytics and a standard process in data science. Nonlinear imputation methods come into play whenever the linear relationship between a response and predictors cannot be linearized by ...
- research-articleApril 2024
Do We Really Need Imputation in AutoML Predictive Modeling?
ACM Transactions on Knowledge Discovery from Data (TKDD), Volume 18, Issue 6Article No.: 147, Pages 1–64https://doi.org/10.1145/3643643Numerous real-world data contain missing values, while in contrast, most Machine Learning (ML) algorithms assume complete datasets. For this reason, several imputation algorithms have been proposed to predict and fill in the missing values. Given the ...
- research-articleMarch 2024
Towards real-world traffic prediction and data imputation: A multi-task pretraining and fine-tuning approach
Information Sciences: an International Journal (ISCI), Volume 657, Issue CFeb 2024https://doi.org/10.1016/j.ins.2023.119972AbstractReal-world traffic prediction is challenging and requires accuracy, efficiency, and generalizability for applications. Most studies used two-step data imputation-prediction procedures or recurrent networks to perform predictions based on imputed ...
- research-articleMarch 2024
Network traffic matrix prediction with incomplete data via masked matrix modeling
Information Sciences: an International Journal (ISCI), Volume 657, Issue CFeb 2024https://doi.org/10.1016/j.ins.2023.119835AbstractTraffic matrix (TM) prediction aims to forecast future traffic data for networks using historical traffic matrices. TM prediction plays a critical role in solving network engineering tasks such as routing management, capacity planning, and ...
- research-articleFebruary 2024
Handling missing values and imbalanced classes in machine learning to predict consumer preference: Demonstrations and comparisons to prominent methods
Expert Systems with Applications: An International Journal (EXWA), Volume 237, Issue PCMar 2024https://doi.org/10.1016/j.eswa.2023.121694AbstractConsumer preference prediction aims to predict consumers’ future purchases based on their historical behavior-level data. Using machine learning algorithms, the prediction results provide evidence to conduct commercial activities and further ...
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- research-articleJuly 2024
Revolutionizing Missing Data Handling with RFKFCM: Random Forest-based Kernelized Fuzzy C-Means
Procedia Computer Science (PROCS), Volume 233, Issue C2024, Pages 66–76https://doi.org/10.1016/j.procs.2024.03.196AbstractMissing values are a prevalent issue, frequently leading to a considerable decline in the quality of data. Therefore, it becomes imperative to adeptly manage missing data. This study presents a technique for imputing missing values that ...
- ArticleNovember 2023
RPL-SVM: Making SVM Robust Against Missing Values and Partial Labels
PRICAI 2023: Trends in Artificial IntelligenceNov 2023, Pages 384–395https://doi.org/10.1007/978-981-99-7025-4_33AbstractWith increased data availability, data quality is the biggest problem in using AI models. The data may suffer from missing values and noisy values. Another major challenge is to get accurate labels for feature vectors in the training data. In ...
- research-articleOctober 2023
A novel neural network for improved in-hospital mortality prediction with irregular and incomplete multivariate data
Neural Networks (NENE), Volume 167, Issue COct 2023, Pages 741–750https://doi.org/10.1016/j.neunet.2023.07.033AbstractAccurate estimation of in-hospital mortality based on patients’ physiological time series data improves the performance of the clinical decision support systems and assists hospital providers in allocating resources. In practice, the data quality ...
- review-articleOctober 2023
A review on missing values for main challenges and methods
AbstractSeveral recent reviews summarize common missing value analysis methods. However, none of them provide a systematic and in-depth summary of the analytical challenges and solutions for dealing with missing values. For the purpose of guiding the ...
Highlights- Analyzed three major difficulties with missing value analysis.
- Provided a comprehensive introduction to deletion and imputation missing approaches.
- Reviewed and analyzed numerous studies and provide useful rules for processing ...
- research-articleOctober 2023
Spatial extrapolation of downscaled geochemical data using conditional GAN
Computers & Geosciences (CGEO), Volume 179, Issue COct 2023https://doi.org/10.1016/j.cageo.2023.105420AbstractMulti-scale geochemical data play an important guiding role in mineral exploration and environmental assessment. However, due to the influence of certain factors such as terrain, sampling cost and time, and instrument detection limit, ...
Highlights- A conditional GAN model was proposed for spatial extrapolation of downscaled geochemical data.
- research-articleOctober 2023
A multistage deep imputation framework for missing values large segment imputation with statistical metrics▪
Applied Soft Computing (APSC), Volume 146, Issue COct 2023https://doi.org/10.1016/j.asoc.2023.110654AbstractThe presence of missing values is a pervasive and unavoidable phenomenon in sensor data. Despite numerous efforts from researchers to address this issue through imputation techniques, particularly in deep learning models, the unique ...
Highlights- Stat indicator for missing data in training/testing.
- New safe imputation method ...
- ArticleNovember 2023
Macsum Aggregation Learning and Missing Values
Symbolic and Quantitative Approaches to Reasoning with UncertaintySep 2023, Pages 453–463https://doi.org/10.1007/978-3-031-45608-4_34AbstractIn recent work, a new kind of aggregation method has been proposed under the name of MacSum aggregation function that can be viewed as an interval valued aggregation function that is controlled by a precise vector of weights. This aggregation can ...
- research-articleSeptember 2023
A novel graph-based missing values imputation method for industrial lubricant data
Computers in Industry (CIIN), Volume 150, Issue CSep 2023https://doi.org/10.1016/j.compind.2023.103937AbstractMissing values are unavoidable in lubricant formulation data in the chemical industry owing to the complexity of lubricant manufacturing. Therefore, imputing missing values using statistical analysis and data mining is essential to obtain ...
Highlights- Missing values are unavoidable in lubricant formulation data in chemical industries.
- We propose a novel graph-based imputation method (GBIM).
- The method considers the feature information and relations between data points.
- The ...
- research-articleAugust 2023
Gaussian processes for missing value imputation
Knowledge-Based Systems (KNBS), Volume 273, Issue CAug 2023https://doi.org/10.1016/j.knosys.2023.110603AbstractA missing value indicates that a particular attribute of an instance of a learning problem is not recorded. They are very common in many real-life datasets. In spite of this, however, most machine learning methods cannot handle missing values. ...
Highlights- A novel approach based on chained GPs, named MGP, is introduced for imputing missing values.
- The method outputs a predictive distribution for each missing value in the dataset.
- The final model can be trained simultaneously and can ...
- research-articleJuly 2023
Policy gradient empowered LSTM with dynamic skips for irregular time series data
Applied Soft Computing (APSC), Volume 142, Issue CJul 2023https://doi.org/10.1016/j.asoc.2023.110314AbstractTime series modelling has been successfully handled by Long Short-Term Memory (LSTM) models. Yet their performance can be severely inhibited by the occurrence of missing values prevalent in many real-life datasets. Many previous studies have been ...
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- research-articleJune 2023
SAITS: Self-attention-based imputation for time series
Expert Systems with Applications: An International Journal (EXWA), Volume 219, Issue CJun 2023https://doi.org/10.1016/j.eswa.2023.119619AbstractMissing data in time series is a pervasive problem that puts obstacles in the way of advanced analysis. A popular solution is imputation, where the fundamental challenge is to determine what values should be filled in. This paper ...
Highlights- Address the missing data problem in time-series analysis tasks by deep learning.
- research-articleJune 2023
Linear-time approximation scheme for k-means clustering of axis-parallel affine subspaces
Computational Geometry: Theory and Applications (COGE), Volume 112, Issue CJun 2023https://doi.org/10.1016/j.comgeo.2023.101981AbstractIn this paper, we present a linear-time approximation scheme for k-means clustering of incomplete data points in d-dimensional Euclidean space. An incomplete data point with Δ > 0 unspecified entries is represented as an axis-parallel ...
- research-articleJanuary 2023
Generalized density attractor clustering for incomplete data
Data Mining and Knowledge Discovery (DMKD), Volume 37, Issue 2Mar 2023, Pages 970–1009https://doi.org/10.1007/s10618-022-00904-6AbstractMean shift is a popular and powerful clustering method for implementing density attractor clustering (DAC). However, DAC is underdeveloped in terms of modeling definitions and methods for incomplete data. Due to DAC’s importance, solving this ...
- research-articleJanuary 2023
An adaptive Laplacian weight random forest imputation for imbalance and mixed-type data
AbstractAs the application of information technology in the medical field is resulting in a large amount of medical data. As early withdrawal and refusal of participants, there are a lot of missing values in medical data. Although various processing ...
- research-articleDecember 2022
Graph convolutional networks for traffic forecasting with missing values
Data Mining and Knowledge Discovery (DMKD), Volume 37, Issue 2Mar 2023, Pages 913–947https://doi.org/10.1007/s10618-022-00903-7AbstractTraffic forecasting has attracted widespread attention recently. In reality, traffic data usually contains missing values due to sensor or communication errors. The Spatio-temporal feature in traffic data brings more challenges for processing such ...