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المؤلفBoeschoten, Sjoerd
المؤلفCatal, Cagatay
المؤلفTekinerdogan, Bedir
المؤلفLommen, Arjen
المؤلفBlokland, Marco
تاريخ الإتاحة2022-11-30T11:23:18Z
تاريخ النشر2023
اسم المنشورExpert Systems with Applications
المصدرScopus
المصدر2-s2.0-85139012625
معرّف المصادر الموحدhttp://dx.doi.org/10.1016/j.eswa.2022.118912
معرّف المصادر الموحدhttp://hdl.handle.net/10576/36775
الملخصRecently pipelines of machine learning-based classification models have become important to codify, orchestrate, and automate the workflow to produce an effective machine learning model. In this article, we propose a framework that combines feature engineering techniques such as data imputation, transformation, and class balancing to compare the performance of different prediction models and select the best final model based on predefined parameters. The proposed framework is extendable and configurable by adding algorithms supported by the CARET package implemented in the R programming language. This framework can generate different machine learning models, which provide comparable results compared to other studies. The framework allows practitioners and researchers to automatically generate different classification models. This research used High-Resolution Orbitrap-based Mass Spectrometers (HRMS) data to create automated prediction models for the first time in literature. We demonstrated the applicability of feature engineering techniques such as data imputation, transformation (e.g., scaling, centering, etc.), and data balancing using several case studies and the proposed semi-automated framework. We showed how the initial prediction models can be improved using the proposed framework. 2022 The Author(s)
راعي المشروعOpen Access funding provided by the Qatar National Library.
اللغةen
الناشرElsevier
الموضوعAutomation; Data balancing; Data imputation; Feature engineering; Feature transformation; Machine learning; Machine learning pipeline
العنوانThe automation of the development of classification models and improvement of model quality using feature engineering techniques
النوعArticle
رقم المجلد213
dc.accessType Open Access


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