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Data Pre-processing

Hui Lin, Ming Li · 2023

Cited by 2

Data Pre-Processing

Peter Christen · 2012

Cited by 3

Data pre-processing.

Polina Suter (11879717), Eva Dazert (13730917), Jack Kuipers (1390278) et al. · Figshare · 2022

Cited by 0Open access

Data pre-processing

Rico Fischer, Fernando Gabriel Luna, Juan Lupiáñez · OSF Preprints (OSF Preprints) · 2024

Cited by 0

Data Pre-processing

Andrei Kirilenko · Springer texts in social sciences · 2025

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Data pre-processing.

Cate Heine (10276751), Cristina Marquez (10276754), Paolo Santi (783893) et al. · Figshare · 2021

<p>We discuss the cleaning process of our Twitter and point of interest data.</p> <p>(PDF)</p>

Cited by 0Open access

Data pre-processing.

Meng Qi (142397), Hongxiang Shao (17054489), Nianfeng Shi (17054492) et al. · Figshare · 2023

<div><p>According to the World Health Organization, cardiovascular diseases are the leading cause of deaths globally. Electrocardiogram (ECG) is a non-invasive approach for detecting heart diseases and reducing the risk of heart disease-related death. However, there are limited numbers of ECG samples and imbalance distribution for existing ECG databases. It is difficult to train practical and efficient neural networks. Based on the analysis and research of many existing ECG databases, this paper conduct an in-depth study on three fine-labeled ECG databases, to extract heartbeats, unify the sampling frequency, and propose a self-processing method of heartbeats, and finally form a unified ECG arrhythmia classification database, noted as Hercules-3. It is separated into training sets (80%) and testing sets (the remaining 20%). In order to verify its capabilities, we have trained a 16-classification fully connected neural network based on Hercules-3 and it achieves an accuracy rate of up to 98.67%. Compared with other data processing, our proposed method improves classification recall by at least 6%, classification accuracy by at least 4%, and F1-score by at least 7%.</p></div>

Cited by 0Open access

Data Pre-processing

Steven Finlay · Palgrave Macmillan UK eBooks · 2010

Some model construction techniques are very flexible when it comes to the format of the predictor variables. However, many methods, and in particular the most popular methods for generating linear models, rely on the relationships between the predictor variables and the dependent variable being linear. This means that better models can be generated if any predictor variables that display non-linear relationships with the dependent variable can be pre-processed (transformed) so that the relationships are more linear.KeywordsPredictor VariableNeural Network ModelInterval VariableModel ConstructionInsurance RateThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Cited by 1

Data pre-processing

Juan Lupiáñez, Rico Fischer, Fernando Gabriel Luna · OSF Preprints (OSF Preprints) · 2024

Use this script with files in Raw data/03-TidyData

Cited by 0

Data Pre-Processing.

Weiyu Zhang (153683), Bingyan Wang (275853), Jingyi Xie (2069899) et al. · Figshare · 2025

<div> “Alternative protein” (AP) refers to protein sources that are different from conventional animal-based proteins. This category encompasses a variety of highly processed plant-based proteins, cultured or cell-based meat, and is primarily driven by factors like sustainability, health, ethical concerns about animal welfare, and the need to feed a growing global population. The aim of this study is to map and understand public discourses around AP foods through social monitoring, in particular, computational analyses of social media big data. Through analyzing the topics and sentiments of Facebook public pages’ English content in the decade of 2014–2024, we found that (1) public attention on plant-based AP foods dropped since 2021, while attention on cell-based AP foods showed an uneven growth; (2) public sentiments towards plant-based AP foods were consistently positive, while sentiments towards cell-based AP foods were clearly more mixed; (3) discussion topics under plant-based AP foods were more practical and diverse than topics under cell-based AP foods; (4) the leading actors talking about AP foods were enterprises & industry, media, and organizations. Overall, plant-based AP foods have generated a more established and diverse public discourse, while cell-based AP foods are still emerging in public conversations and continue to face greater barriers to widespread understanding and acceptance. </div>

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Data pre-processing

Marcela Xavier Ribeiro, Mônica Ribeiro Porto Ferreira, Caetano Traina et al. · 2008

Data pre-processing is a key element to improve the accuracy of data mining algorithms. In the pre-processing step, the data are treated in order to make the mining process achievable and effective. Data discretization and feature selection are two important tasks that can be performed prior to the learning phase and can significantly reduce the processing effort of the data mining algorithm. In this paper, we present Omega, a new algorithm for data discretization and feature selection. Omega performs simultaneously data discretization and feature selection. We validated Omega by comparing it with other well-known algorithms for data discretization (1R, ChiMerge and Chi2) and feature selection (DTM, Relief and Chi2). The experiments compared the effects of the pre-processing techniques in the results of the C4.5 algorithm (a well-known decision tree-based classifier). In the results, the data discretization provided by Omega generates the decision tree with one of the smallest average of the number of nodes and the feature selection given by Omega leads to one of the smallest average of error rate. These results indicates that Omega is well-suited to perform both, data discretization and feature selection, being highly appropriate for pre-processing data for data mining tasks.

Cited by 10

Data Pre-processing

Laurie A. Schintler, Connie L. McNeely · 2022

Cited by 0

data pre-processing

2024

Citation: 'data pre-processing' in the IUPAC Compendium of Chemical Terminology, 5th ed.; International Union of Pure and Applied Chemistry; 2025. Online version 5.0.0, 2025. 10.1351/goldbook.10048 • License: The IUPAC Gold Book is licensed under Creative Commons Attribution-ShareAlike CC BY-SA 4.0 International for individual terms. Requests for commercial usage of the compendium should be directed to IUPAC.

Cited by 0

Data Pre-processing

Parteek Bhatia · 2025

Machine learning has become a dominant problem-solving technique in the modern world, with applications ranging from search engines and social media to self-driving cars and artificial intelligence. This lucid textbook presents the theoretical foundations of machine learning algorithms, and then illustrates each concept with its detailed implementation in Python to allow beginners to effectively implement the principles in real-world applications. All major techniques, such as regression, classification, clustering, deep learning, and association mining, have been illustrated using step-by-step coding instructions to help inculcate a 'learning by doing' approach. The book has no prerequisites, and covers the subject from the ground up, including a detailed introductory chapter on the Python language. As such, it is going to be a valuable resource not only for students of computer science, but also for anyone looking for a foundation in the subject, as well as professionals looking for a ready reckoner.

Cited by 0

Data pre-processing

Magda Baniukiewicz · OSF Preprints (OSF Preprints) · 2016

Component contains all files used to clean and prepare data before running simulations (pre-processing)

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Data pre-processing

María Julieta Aguirre · OSF Preprints (OSF Preprints) · 2024

Cited by 0

Data pre-processing.

Hui Yuan (402180), Mingzhu Liu (1727896), Yushan Qiu (4685401) et al. · Figshare · 2025

<div> The development of single-cell multi-omics sequencing technologies has enabled the simultaneous analysis of multi-omics data within the same cell. Accurate clustering of these cells is crucial for downstream analyses of complex biological functions. Despite significant advances in multi-omics integration approaches, current methodologies exhibit two major limitations. First, they inadequately incorporate prior biological knowledge from various omic layers. Second, these methods often conduct independent dimensionality reduction on individual omic datasets, thereby failing to capture the intrinsic complementary information and potentially overlooking crucial cross-platform interactions. Motivated by these, this study investigates a non-negative matrix factorization model called PLNMFG, which integrates the unified latent representation learning that retains the features between and within omics and the cluster structure learning that retains the intrinsic structure of the data into one joint framework. Specially, PLNMFG performs adaptive imputation to handle dropout events and uses prior pseudo-labels as constraints during the process of collective non-negative matrix factorization, as a result, a more robust latent representation that preserves the double similarity information is obtained. Graph Laplacian constraint is applied during clustering which further preserves structure characteristic of multi-omics data. In addition, the weight of each omic is adaptively learned based on the omic contribution. A series of experiments on 8 benchmark datasets show that our model performs well in terms of clustering accuracy and computational efficiency. </div>

Cited by 0Open access

Data pre-processing.

Betzaida Cuevas-Córdoba (6803738), Cristóbal Fresno (9162806), Joshua I. Haase-Hernández (11608072) et al. · Figshare · 2021

&lt;p&gt;a) Raw reads and mean quality value across each base position. b) Distribution of fragment sizes (read lengths) after trimming. c) Mean quality value across each base position in the reads after trimming. d) Number of reads with average quality scores after trimming.&lt;/p&gt; &lt;p&gt;(TIF)&lt;/p&gt;

Cited by 0Open access

Data Pre-processing

Brian Malley, Daniele Ramazzotti, Joy T. Wu · 2016

In this chapter, the reader will gain knowledge and practical skills about preparing raw clinical data for secondary statistical analysis.

Cited by 40Open access

Data pre-processing methodology.

Igor Vitor Teixeira (11944950), Morgana Thalita da Silva Leite (16015174), Flávio Leandro de Morais Melo (16015177) et al. · Figshare · 2023

&lt;div&gt;&lt;p&gt;Background&lt;/p&gt;&lt;p&gt;Communicable diseases represent a huge economic burden for healthcare systems and for society. Sexually transmitted infections (STIs) are a concerning issue, especially in developing and underdeveloped countries, in which environmental factors and other determinants of health play a role in contributing to its fast spread. In light of this situation, machine learning techniques have been explored to assess the incidence of syphilis and contribute to the epidemiological surveillance in this scenario.&lt;/p&gt;&lt;p&gt;Objective&lt;/p&gt;&lt;p&gt;The main goal of this work is to evaluate the performance of different machine learning models on predicting undesirable outcomes of congenital syphilis in order to assist resources allocation and optimize the healthcare actions, especially in a constrained health environment.&lt;/p&gt;&lt;p&gt;Method&lt;/p&gt;&lt;p&gt;We use clinical and sociodemographic data from pregnant women that were assisted by a social program in Pernambuco, Brazil, named &lt;i&gt;Mãe Coruja Pernambucana&lt;/i&gt; Program (PMCP). Based on a rigorous methodology, we propose six experiments using three feature selection techniques to select the most relevant attributes, pre-process and clean the data, apply hyperparameter optimization to tune the machine learning models, and train and test models to have a fair evaluation and discussion.&lt;/p&gt;&lt;p&gt;Results&lt;/p&gt;&lt;p&gt;The AdaBoost-BODS-Expert model, an Adaptive Boosting (AdaBoost) model that used attributes selected by health experts, presented the best results in terms of evaluation metrics and acceptance by health experts from PMCP. By using this model, the results are more reliable and allows adoption on a daily usage to classify possible outcomes of congenital syphilis using clinical and sociodemographic data.&lt;/p&gt;&lt;/div&gt;

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