El Shawi, Radwa, juhendajaEldeeb, Hassan, juhendajaKaya, KayahanTartu Ülikool. Loodus- ja täppisteaduste valdkondTartu Ülikool. Arvutiteaduse instituut2023-08-312023-08-312022https://hdl.handle.net/10062/91929Building well-performing machine learning pipelines requires the use of feature engineering. However, building highly predictive features takes time and requires subject-matter expertise. Although automated feature engineering research has recently gained a lot of attention from both academia and industry, the scalability and efficiency of the current methods and tools are still essentially subpar. To this end, we proposed meta-learning techniques to improve the performance of two automated machine learning frameworks; BigFeat and AutoFeat. Extensive experiments were conducted on 17 and 10 datasets for Bigfeat and AutoFeat, respectively. The results show that the proposed meta-learning techniques achieved an average improvement of F1-Score = 1.51% on BigFeat and an average improvement of F1-Score = 1.11% on AutoFeat.engopenAccessAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/Feature EngineeringAutomated Machine LearningMeta-learningmagistritöödinformaatikainfotehnoloogiainformaticsinfotechnologyThesis