论文标题
通过遗传算法估计内核密度
Kernel Density Estimation by Genetic Algorithm
论文作者
论文摘要
这项研究提出了一种通过遗传算法估计多元核密度估计的数据凝结方法。首先，我们提出的算法生成了给定尺寸的多个子样本，并从原始样本中替换。在遗传算法的术语中，分别将子样本及其组成的数据点分别视为$ \ it {colomosome} $和$ \ it {gene} $。其次，每对子样本都繁殖两个新的子样本，每个数据点都面对$ \ it {crossover} $，$ \ it {mutation} $，或$ \ it {reproduction} $具有一定的可能性。在健身价值方面的主要子样本是下一代继承的。该过程是一代重复的，并在完成后将内核密度估计器的稀疏表示。我们从模拟研究中证实，所得的估计器的性能比其他众所周知的密度估计器更好。
This study proposes a data condensation method for multivariate kernel density estimation by genetic algorithm. First, our proposed algorithm generates multiple subsamples of a given size with replacement from the original sample. The subsamples and their constituting data points are regarded as $\it{chromosome}$ and $\it{gene}$, respectively, in the terminology of genetic algorithm. Second, each pair of subsamples breeds two new subsamples, where each data point faces either $\it{crossover}$, $\it{mutation}$, or $\it{reproduction}$ with a certain probability. The dominant subsamples in terms of fitness values are inherited by the next generation. This process is repeated generation by generation and brings the sparse representation of kernel density estimator in its completion. We confirmed from simulation studies that the resulting estimator can perform better than other well-known density estimators.
