一种处理集合生成中背包问题的随机方法
A stochastic approach to handle knapsack problems in the creation of ensembles
摘要：
基于集成的方法是非常流行的方法，通过汇总各个选民的意见来提高决策的准确性。共同点是最大限度地提高准确性;然而，如果将增量成本也分配给个体选民，则会出现自然限制。因此，我们调查创建合奏下的成员的总成本的额外约束。这个任务可以被公式化为背包问题，其中能量是由一些聚集规则形成的集成精度。然而，通常应用的聚合规则导致不可分离的能量函数，这使得常见的求解工具--例如动态规划--失效。我们引入了一种新的随机方法，认为能源的联合概率函数的成员精度。这种类型的知识可以有效地纳入随机搜索过程作为停止规则，因为我们有预期的准确性，或者，找到更准确的合奏的概率的信息。实验分析所创建的模式分类器和对象检测器的合奏证实了我们的方法的效率。此外，我们提出了一种新的随机搜索策略，更好地适应的能量，比一般的方法，如模拟退火。
英文摘要：
Ensemble-based methods are highly popular approaches that increase the accuracy of a decision by aggregating the opinions of individual voters. The common point is to maximize accuracy; however, a natural limitation occurs if incremental costs are also assigned to the individual voters. Consequently, we investigate creating ensembles under an additional constraint on the total cost of the members. This task can be formulated as a knapsack problem, where the energy is the ensemble accuracy formed by some aggregation rules. However, the generally applied aggregation rules lead to a nonseparable energy function, which takes the common solution tools -- such as dynamic programming -- out of action. We introduce a novel stochastic approach that considers the energy as the joint probability function of the member accuracies. This type of knowledge can be efficiently incorporated in a stochastic search process as a stopping rule, since we have the information on the expected accuracy or, alternatively, the probability of finding more accurate ensembles. Experimental analyses of the created ensembles of pattern classifiers and object detectors confirm the efficiency of our approach. Moreover, we propose a novel stochastic search strategy that better fits the energy, compared with general approaches such as simulated annealing.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
