27 papers · ranked by Valyu relevance
Tlamelo Emmanuel, Thabiso Maupong, Dimane Mpoeleng, Thabo Semong + 2 more
'Banyatsang Mphago' 'Oteng Tabona'] Machine learning has been the corner stone in analysing and extracting information from data and often a problem of missing values is encountered. Missing values occur because of various factors like missing completely at random, missing at random or missing not at random. All these…
Anny K. G. Rodrigues, Raydonal Ospina, Marcelo R. P. Ferreira, Afnizanfaizal Abdullah
'Afnizanfaizal Abdullah'] Many machine learning procedures, including clustering analysis are often affected by missing values. This work aims to propose and evaluate a Kernel Fuzzy C-means clustering algorithm considering the kernelization of the metric with local adaptive distances (VKFCM-K-LP) under three types of…
Youran Zhou, Mohamed Reda Bouadjenek, Sunil Aryal
Missing data is a pervasive challenge spanning diverse data types, including tabular, sensor data, time-series, images and so on. Its origins are multifaceted, resulting in various missing mechanisms. Prior research in this field has predominantly revolved around the assumption of the Missing Completely At Random…
Monica Casella, Nicola Milano, Pasquale Dolce, Davide Marocco
Introduction Missing data in psychometric research presents a substantial challenge, impacting the reliability and validity of study outcomes. Various factors contribute to this issue, including participant non-response, dropout, or technical errors during data collection. Traditional methods like mean imputation or…
Jahan C. Penny‐Dimri, Christoph Bergmeir, Julian A. Smith
Most practical data science problems encounter missing data. A wide variety of solutions exist, each with strengths and weaknesses that depend upon the missingness-generating process. Here we develop a theoretical framework for training and inference using only observed variables enabling modeling of incomplete…
Youran Zhou, Sunil Aryal, Mohamed Reda Bouadjenek
Missing data poses a significant challenge in data science, affecting decision-making processes and outcomes. Understanding what missing data is, how it occurs, and why it is crucial to handle it appropriately is paramount when working with real-world data, especially in tabular data, one of the most commonly used data…
Mikko Särkkä, Sami Myöhänen, Kaloyan Marinov, Inka Saarinen + 3 more
Modern clinical genetic tests utilize next-generation sequencing (NGS) approaches to comprehensively analyze genetic variants from patients. Out of these millions of variants, clinically relevant variants that match the patient’s phenotype need to be identified accurately within a rapid timeframe that facilitates…
Muhammad Salar Khan, Larissa-Margareta Batrancea
Within the national innovation system literature, the low- and middle-income countries (LMICs) eligible for the World Bank’s International Development Association (IDA) support, are rarely part of empirical discourses on growth, development, and innovation. One major issue hindering empirical analyses in LMICs is the…
Sara Johansson Fernstad, Sarah Alsufyani, Silvia Del Din, Alison Yarnall + 1 more
'Alison Yarnall' 'Lynn Rochester'] This paper contributes a set of quality metrics for identification and visual analysis of structured missingness in high-dimensional data. Missing values in data are a frequent challenge in most data generating domains and may cause a range of analysis issues. Structural missingness…
Adrienne Kline, Yuan Luo
Most datasets suffer from partial or complete missing values, which has downstream limitations on the available models on which to test the data and on any statistical inferences that can be made from the data. Several imputation techniques have been designed to replace missing data with stand in values. The various…
Barbora Rehák Bučková, Charlotte Fraza, Cecilie Koldbæk Lemvigh, Camilla Bärthel Flaaten + 11 more
Missing data remain a ubiquitous and critical challenge in large-scale clinical studies. Despite advances in imputation, most existing methods fail to address structured missingness, where data are missing according a deterministic pattern and which arise due to systematic patterns introduced by experimental design…
Seongmin Kim, Jaewon Oh, Hye-Young Ko, Jeongmin Park + 1 more
Missing data is a common issue in various fields such as medicine, social sciences, and natural sciences, and it poses significant challenges for accurate statistical analysis. Although numerous imputation methods have been proposed to address this issue, many of them fail to adequately capture the complex dependency…
Mithilesh Prakash, Jussi Tohka
We introduce a new subtype of ‘Missing Not at Random’ (MNAR) data, where the missingness is correlated with the labels (y) to be predicted, termed (y)-dependent MNAR. We demonstrate that this subtype can significantly bias the estimation of performance metrics in typical machine learning tasks. Unbiased error…
Mustafa Buyukozkan, Elisa Benedetti, Jan Krumsiek
High-dimensional omics datasets frequently contain missing data points, which typically occur due to concentrations below the limit of detection (LOD) of the profiling platform. The presence of such missing values significantly limits downstream statistical analysis and result interpretation. Two common techniques to…
Huiting Ou, Anuradha Surendra, Graeme S.V. McDowell, Emily Hashimoto-Roth + 3 more
Missing values are often unavoidable in modern high-throughput measurements due to various experimental or analytical reasons. Imputation, the process of replacing missing values in a dataset with estimated values, plays an important role in multivariate and machine learning analyses. Three missingness patterns have…
Muhammad Ishaq, Laila iftikhar, Majid Khan, Asfandyar Khan + 1 more
'Arshad Khan'] This study explored the use of machine learning algorithms for predicting and imputing missing values in categorical datasets. We focused on ensemble models that use the error correction output codes (ECOC) framework, including SVM-based and KNN-based ensemble models, as well as an ensemble classifier…
Karl Schweizer, Andreas Gold, Dorothea Krampen
In modeling missing data, the missing data latent variable of the confirmatory factor model accounts for systematic variation associated with missing data so that replacement of what is missing is not required. This study aimed at extending the modeling missing data approach to tetrachoric correlations as input and at…
Hugo Morvan, Jonas Agholme, Bjorn Eliasson, Katarina Olofsson + 3 more
Missing data is a prevalent issue in many applications, including large medical registries such as the Swedish Healthcare Quality Registries, potentially leading to biased or inefficient analyses if not handled properly. Multiple Imputation by Chained Equations (MICE) is a popular and versatile method for handling…
Martin Seifrid, Stanley Lo, Dylan Choi, Gary Tom + 12 more
Martin Seifrid 1 , Stanley Lo 2 , Dylan G. Choi 3 , Gary Tom 2 , My Linh Le 3 , Kunyu Li 3 , Rahul Sankar 3 , Hoai-Thanh Vuong 3 , Hiba Wakidi 3 , Ahra Yi 3 , Ziyue Zhu 3 , Nora Schopp 3 , Aaron Peng 3 , Benjamin Luginbuhl 3 , Thuc-Quyen Nguyen 3 , Alán Aspuru-Guzik 2
Jessica Ryan-Despraz, Amanda Wissler
Missing data is a prevalent problem in bioarchaeological research and imputation could provide a promising solution. This work simulated missingness on a control dataset (481 samples × 41 variables) in order to explore imputation methods for mixed data (qualitative and quantitative data). The tested methods included…
Diba Behnoudfar, Cory Simon, Joshua Schrier
Aqueous, two-phase systems (ATPSs) may form upon mixing two solutions of independently water-soluble compounds. Many separation, purification, and extraction processes rely on ATPSs. Predicting the miscibility of solutions can accelerate and reduce the cost of the discovery of new ATPSs for these applications. Whereas…
Bartłomiej Fliszkiewicz, Marcin Sajdak
The aim of the following research is to assess the applicability of calculated quantum properties of molecular fragments as molecular descriptors in machine learning classification task. The research is based on bio-concentration and QM9-extended databases. A number of compounds with results from quantum-chemical…
Xing Chen, Na Zhang, Xiaohui Yang, Chunyan Wang + 5 more
In daily life, two common algorithms are used for collecting medical disease data: data integration of medical institutions and questionnaires. However, these statistical methods require collecting data from the entire research area, which consumes a significant amount of manpower and material resources. Additionally…
Authors not listed
Chemical data is fundamentally sparse, with molecular structures serving as database keys for countless properties. Current machine learning methods map structures to properties with remarkable accuracy, yet they do not leverage available property information when predicting unknowns, creating unutilized partial…
Authors not listed
Solute carrier (SLC) transporters constitute the largest family of membrane transport proteins in humans. They facilitate the movement of ions, neurotransmitters, nutrients, and drugs. Given their critical role in regulating cellular physiology, they are important therapeutic targets for neurological and psychological…
Sangjoon Lee, Clio Chen, Griheydi Garcia, Anton Oliynyk
Materials informatics uses data-driven approaches for the study and discovery of materials. Features or descriptors are the crucial components in generating reliable and accurate machine-learning models. While general data can be acquired through public and commercial sources, features must be tailored for a specific…
Chonghuan Zhang, Adarsh Arun, Alexei Lapkin
Computer Aided Synthesis Planning (CASP) development of reaction routes requires understanding of complete reaction structures. However, most reactions in the current databases are missing reaction co-participants. Although reaction prediction and atom mapping tools can predict major reaction participants and trace…