Determination of regional variants in the versification of Estonian folksongs using an interpretable fuzzy rule-based classifier

A. Riid, M. Sarv
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引用次数: 6

Abstract

In this paper, a method of hierarchical clustering and a selection of fuzzy classification algorithms are applied successively to the data set that contains measured characteristics of folk verses collected from 104 historical parishes of Estonia. The aim of the study is to detect the groups of parishes that are similar in terms of folk verse characteristics and to give us insight into the reasoning that the separation into these groups is based upon. The process of classification separates the initial groups into further subsets represented by fuzzy rules, which can be analyzed thanks to the interpretability of such rules. To emphasize the latter, most important features in individual rules are brought out by rule compression. The results of the analysis are backed by what is known from linguistic sciences.
使用可解释的模糊规则分类器确定爱沙尼亚民歌版本中的区域变体
本文将层次聚类方法和模糊分类算法的选择先后应用于包含爱沙尼亚104个历史教区的民间诗歌测量特征的数据集。本研究的目的是发现在民间诗歌特征方面相似的教区群体,并为我们了解这些群体划分的原因提供依据。分类过程将初始组划分为由模糊规则表示的进一步子集,由于这些规则的可解释性,可以对这些子集进行分析。为了强调后者,在单个规则中最重要的特征是通过规则压缩得到的。分析结果得到了语言科学知识的支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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