Abstract

In recent years, Machine Learning (ML) and Automated Machine Learning (Auto-ML) have attracted significant attention. The ML pipeline involves repetitive tasks such as data preprocessing, feature engineering, model selection, and hyperparameter tuning. Developing a machine learning model demands considerable time for development, stress testing, and numerous experiments. Additionally, constructing a model with a limited search space of pipeline steps and various algorithms can take hours. As a result, Auto-ML has become widely adopted to reduce the time and effort required for these tasks. However, most current Auto-ML frameworks primarily concentrate on algorithm selection and hyperparameter optimization, known as CASH, while overlooking other critical ML pipeline steps like data preprocessing and feature engineering. This limited focus often results in suboptimal pipelines for specific datasets. Moreover, a significant number of frameworks overlook the integration of meta-learning, resulting in the promotion of high-performing pipelines customized for individual tasks rather than a universally optimal solution. Consequently, this deficiency necessitates the quest for a new pipeline tailored to each unique task, further underscoring the importance of a more comprehensive approach in Auto-ML frameworks. Additionally, while some Auto-ML frameworks address the entire pipeline, they often overlook the challenges posed by imbalanced datasets. To address these issues, we propose a novel and efficient meta-learning Auto-ML framework that effectively manages imbalanced datasets. The proposed framework outperforms state-of-the-art results in terms of accuracy, precision, recall, and time, demonstrating, on average, more than 5% improvement compared to the existing auto-ML frameworks. This paper also illustrates how our proposed framework outperforms current state-of-the-art solutions.

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