2021 | OriginalPaper | Buchkapitel
An Empirical Analysis of Integrating Feature Extraction to Automated Machine Learning Pipeline
verfasst von : Hassan Eldeeb, Shota Amashukeli, Radwa El Shawi
Erschienen in: Pattern Recognition. ICPR International Workshops and Challenges
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Abstract
FE
) is one of the most time-consuming steps in building machine learning pipelines. It requires a deep understanding of the domain and data exploration to discover relevant hand-crafted features from raw data. In this work, we empirically evaluate the impact of integrating an automated feature extraction tool (AutoFeat
) into two automated machine learning frameworks, namely, Auto-Sklearn
and TPOT
, on their predictive performance. Besides, we discuss the limitations of AutoFeat
that need to be addressed in order to improve the predictive performance of the automated machine learning frameworks on real-world datasets.