A compressed sensing approach for efficient ensemble learning
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.
compressed sensing ensemble learning classification classifier ensemble sparse reconstruction roulette-wheel selection kappa-error