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MCAL hybridizes particle swarm optimization(PSO)and cost-sensitive active learning(CAL)to improve the classification accuracy with multi-oracles and reduce the cost of labeling instances sampled.
为了提高分类精度和减少标注代价,该方法结合粒子群优化和代价敏感主动学习。
杂交
MCAL hybridizes particle swarm optimization(PSO)and cost-sensitive active learning(CAL)to improve the classification accuracy with multi-oracles and reduce the cost of labeling instances sampled.
为了提高分类精度和减少标注代价,该方法结合粒子群优化和代价敏感主动学习。