Improving Automated Feature Engineering Using Meta-learning Based Techniques
Name
Kayahan Kaya
Abstract
Building 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 metalearning 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 metalearning techniques achieved an average improvement of F1-Score = 1.51% on BigFeat and an average improvement of F1-Score = 1.11% on AutoFeat.
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 metalearning 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 metalearning techniques achieved an average improvement of F1-Score = 1.51% on BigFeat and an average improvement of F1-Score = 1.11% on AutoFeat.
Graduation Thesis language
English
Graduation Thesis type
Master - Computer Science
Supervisor(s)
Radwa El Shawi, Hassan Eldeeb
Defence year
2022