An effective method in machine learning often involves considerable experience with algorithms and domain expertise. Many existing machine learning methods highly rely on feature selection which are always domain-specific. However, the intervention by data scientists is time-consuming and labor-intensive. To meet this challenge, we propose a Feature Transferring Autonomous machine learning Pipeline (FTAP) to improve efficiency and performance. The proposed FTAP has been extensively evaluated on different modalities of data covering audios, images, and texts. Experimental results demonstrate that the proposed FTAP not only outperforms state-of-the-art methods on ESC-50 dataset with multi-class audio classification but also has good performance in distant domain transfer learning. Furthermore, FTAP outperforms TPOT, a state-of-the-art autonomous machine learning tool, on learning tasks. The quantitative and qualitative analysis proves the feasibility and robustness of the proposed FTAP. (C) 2022 Elsevier Inc. All rights reserved.
NSTL
Elsevier
