Yash Aparajit, Saurabh Sanap, Nikhil Kumar et al. · International Research Journal of Modernization in Engineering Technology and Science · 2023
Data preprocessing is a critical step in the data analysis process, with a direct impact on the quality of analytical outcomes. In this paper, we introduce the Data Preprocessing Interface, a web-based tool designed to enhance and streamline data preprocessing. This interface addresses the challenges associated with data cleaning, transformation, and organization by offering a user-friendly and efficient platform. We present the motivation behind our project, its methodology, and system architecture. Our research demonstrates the potential for significant improvements in data preprocessing, allowing data scientists and analysts to work with cleaner, more reliable data. The results not only highlight the expeditious data preprocessing but also emphasize the enhanced user experience. We conclude with insights into the future developments of the Data Preprocessing Interface.
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Ilya V. Narsky, Prof. Frank C. Porter · 2013
This chapter contains sections titled: Categorical Variables Missing Values Outliers Exercises References
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David Bermbach, Erik Wittern, Stefan Tai · 2017
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Parteek Bhatia · Cambridge University Press eBooks · 2019
Chapter Objectives✓ To understand the need for data preprocessing.✓ To identify different phases of data preprocessing such as data cleaning, data integration, data transformation and data reduction
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Marcus J. Neuer · 2024
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Elizaveta Zinovyeva · 2021
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WSPC/HEP eBooks · 2023
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Thomas A. Runkler · Data analytics · 2024
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Vašíček, Radek · Brno University of Technology Digital Library (Brno University of Technology) · 2008
Tato diplomová práce se zaměřuje na problematiku předzpracováním dat. První část se zabývá přehledem a popisem charakteristických testů pro popis atributů, metodami pro práci s daty a atributy. Druhá část práce se popisuje práci s programem Rapidminer. Věnuje se jednotlivým funkcím předzpracování v tomto programu popisuje jejich funkci. Ve třetí části je srovnání výsledku při použití metod předzpracování a bez předzpracování dat.
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Aaron C. Weidman · OSF Preprints (OSF Preprints) · 2024
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Joachim Taelman, Bogdan Mijovic, Sabine Van Huffel et al. · 2012
Single channel-blind source separation, Ensemble empirical mode decomposition, ECG interference artifact,
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Gian Luca Lancia (13991392), Mattia Eluchans (14341772), Marco D’Alessandro (397205) et al. · Figshare · 2023
<p>(A) We transformed the original map shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1010829#pcbi.1010829.g002" target="_blank">Fig 2</a> into a graph of 66 connected nodes (which we coloured in arbitrary ways for ease of identification). Note that for each node, up to four (north, south, east, and west) actions are available. (B) Maze graph with arrows representing the default policy (the policy is uniformly distributed in the node without an arrow). See the main text for explanation.</p>
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Adam J Parker, Maria Sablik, Emma Karlsson et al. · OSF Preprints (OSF Preprints) · 2023
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Wang, Qiran · Figshare · 2025
The hyperspectral data underwent a multi-step preprocessing pipeline to enhance spectral quality and ensure consistency across samples. First, Principal Component Analysis (PCA) was applied in ENVI to reduce dimensionality while retaining over 99.95% of the total variance. Subsequently, Standard Normal Variate (SNV) transformation was performed to eliminate scattering effects and baseline drift, followed by Savitzky–Golay (SG) filtering and first-order derivative processing to smooth spectra and highlight subtle features. Finally, Z-score normalization was applied to standardize feature magnitudes, improving model stability and convergence in subsequent classification tasks.
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Lin Wei (70005), Haoxiang Ran (17480543), Yuping Yin (3617510) et al. · Figshare · 2024
<div><p>Addressing the challenges in effectively extracting multi-scale features and preserving pose information during hyperspectral image (HSI) classification, a Multi-Scale Depthwise Separable Capsule Network (MDSC-Net) is proposed in this article for HSI classification. Initially, hierarchical features are extracted by MDSC-Net through the employment of parallel multi-scale convolutional kernels, while computational complexity is reduced via depthwise separable convolutions, thus reducing the overall computational load and achieving efficient feature extraction. Subsequently, to enhance the translational invariance of features and reduce the loss of pose information, features of various scales are processed in parallel by independent capsule networks, with improvements in max pooling achieved through dynamic routing. Lastly, features of different scales are concatenated and integrated through the concatenate operation, thereby facilitating precise analysis of multi-level information in the hyperspectral image classification process. Experimental comparisons demonstrate that MDSC-Net achieves average accuracies of 94%, 98%, and 99% on the Kennedy Space Center, University of Pavia, and Salinas datasets, respectively, indicating a significant performance advantage over recent HSI classification models and validating the effectiveness of the proposed model.</p></div>
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Jieun Kim (427023), Dong-Uk Hwang (14100814), Edwin J. Son (10922285) et al. · Figshare · 2022
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Christo El Morr, Manar Jammal, Hossam Ali-Hassan et al. · RePEc: Research Papers in Economics
Abstract Preprocessing is the practice of cleaning, altering, and reorganizing raw data prior to processing and analysis, which is also known as data preparation [1]. It is an important step before processing and usually entails reformatting, adjusting, and integrating datasets to improve the information contained within them. Even though data preprocessing can be an onerous task, it is necessary as a precondition for putting data into context and reducing the possibility of bias [2]. An Aberdeen Group study states that data preprocessing refers to any activity taken in order to improve the quality, usability, accessibility, and portability of data [3]. In a poll published in Forbes, data scientists reported that they spend 60% of their time on data preprocessing (Fig. 4.1).
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Demertzis, Konstantinos · 2019
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Bernard J. Jansen, Kholoud K. Aldous, Joni Salminen et al. · Synthesis lectures on information concepts, retrieval, and services · 2023
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Christian Schneijderberg, Oliver Wieczorek, Isabel Steinhardt · 2026
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