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西田 喜平次 所属 京都産業大学 経営学部 職種 准教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2022/11 |
| 形態種別 | 研究論文 |
| 査読 | 査読あり |
| 標題 | Kernel Density Estimation by Genetic Algorithm |
| 執筆形態 | その他 |
| 掲載誌名 | Journal of Statistical Computation and Simulation |
| 掲載区分 | 国外 |
| 出版社・発行元 | Taylor and Francis |
| 著者・共著者 | Kiheiji NISHIDA |
| 概要 | 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 chromosome and gene, respectively, in the terminology of genetic algorithm. Second, each pair of subsamples breeds two new subsamples, where each data point faces either crossover, mutation, or 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.
(Accepted for publication on 6th Oct. 2022. Published online on 14th Nov. 2022.) |
| DOI | 10.1080/00949655.2022.2134379 |
| PermalinkURL | https://arxiv.org/abs/2203.01535 |