A compressed sensing approach for efficient ensemble learning

Lin Li, Rustam Stolkin, Licheng Jiao, Fang Liu, Shuang Wang

Abstract: Highlights•A compressed sensing approach for efficient ensemble learning is proposed.•A new performance evaluation method (the roulette-wheel kappa-error) is proposed.•The ensemble of classifiers is posed as a compressed sensing problem.•Roulette-wheel is used to select pairs of classifiers based on their weightings.•The proposed method is testified by 25 different public data sets.