A novel meta-learning framework: Multi-features adaptive aggregation method with information enhancer

Hailiang Ye, Yi Wang, Feilong Cao

Abstract: Highlights•A multi-features adaptive aggregation meta-learning method is proposed.•An information enhancer is built to capture more valuable information.•A multi-features aggregation classifier is designed in an adaptive way.•A mixed loss is presented by combining the classification and reconstruction losses.