  Automated machine learning (AutoML) aims for constructing machine learning
(ML) pipelines automatically. Many studies have investigated efficient methods
for algorithm selection and hyperparameter optimization. However, methods for
ML pipeline synthesis and optimization considering the impact of complex
pipeline structures containing multiple preprocessing and classification
algorithms have not been studied thoroughly. In this paper, we propose a
data-centric approach based on meta-features for pipeline construction and
hyperparameter optimization inspired by human behavior. By expanding the
pipeline search space incrementally in combination with meta-features of
intermediate data sets, we are able to prune the pipeline structure search
space efficiently. Consequently, flexible and data set specific ML pipelines
can be constructed. We prove the effectiveness and competitiveness of our
approach on 28 data sets used in well-established AutoML benchmarks in
comparison with state-of-the-art AutoML frameworks.
