機械学習モデルからの知識抽出と生命情報学への応用

京都大学新制・課程博士博士(情報学)甲第23397号情博第766号新制||情||131(附属図書館)京都大学大学院情報学研究科知能情報学専攻(主査)教授 阿久津 達也, 教授 山本 章博, 教授 鹿島 久嗣学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDFA

<Bioinformatics Center>Mathematical Bioinformatics

This Annual Report covers from 1 January to 31 December 202

Analysis and Control of Biological Information Networks

令和2年度 京都大学化学研究所 スーパーコンピュータシステム 利用報告

Analysis and Control of Biological Information Networks

令和2年度 京都大学化学研究所 スーパーコンピュータシステム 利用報告

Publications

This Annual Report covers from 1 January to 31 December 202

ICR ANNUAL REPORT 2020 (Volume 27)[All Pages]

This Annual Report covers from 1 January to 31 December 202

Extracting boolean and probabilistic rules from trained neural networks

This paper presents two approaches to extracting rules from a trained neural network consisting of linear threshold functions. The first one leads to an algorithm that extracts rules in the form of Boolean functions. Compared with an existing one, this algorithm outputs much more concise rules if the threshold functions correspond to 1-decision lists, majority functions, or certain combinations of these. The second one extracts probabilistic rules representing relations between some of the input variables and the output using a dynamic programming algorithm. The algorithm runs in pseudo-polynomial time if each hidden layer has a constant number of neurons. We demonstrate the effectiveness of these two approaches by computational experiments