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ヤマモト エイコ
Eiko Yamamoto
山本 英子 所属 経済情報学部 職種 教授 |
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| 言語種別 | 英語 |
| 発行・発表の年月 | 2017/08 |
| 形態種別 | 研究論文（国際会議プロシーディングス） |
| 査読 | 査読あり |
| 標題 | Finding Association Rules by Direct Estimation of Likelihood Ratios |
| 執筆形態 | 共著 |
| 掲載誌名 | Proc. of The 2017 International Conference On Advanced Informatics: Concepts, Theory And Application (ICAICTA 2017) |
| 掲載区分 | 国外 |
| 総ページ数 | 5 |
| 著者・共著者 | Kento Kawakami, Masato Kikuchi, Mitsuo Yoshida, Eiko Yamamoto, Kyoji Umemura |
| 概要 | In this paper, we propose a cost function that corresponds to the mean square errors between estimated values and true values of conditional probability in a discrete distribution. We then obtain the values that minimize the cost function. This minimization approach can be regarded as the direct estimation of likelihood ratios because the estimation of conditional probability can be regarded as the estimation of likelihood ratio by the definition of conditional probability. When we use the estimated value as the strength of association rules for data mining, we find that it outperforms a well-used method called Apriori. |