模式进化研究中基于顺序的特征选择验证

IF 0.9 4区计算机科学 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Intelligent Data Analysis Pub Date : 2023-10-12 DOI:10.3233/ida-230326

Gábor Hosszú

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引用次数: 0

摘要

本文阐述了逐步发展的模式系统的思想。这些系统涉及符号通信，包括符号、语法和布局规则。有些模式系统会随着时间而改变，比如历史脚本。对模式系统的科学研究被称为模式演化研究，而脚本信息学关注的是对脚本演化的建模。符号序列由图案系统中的符号组成，而图序列则是应用了特定技术的符号序列。本文描述了一种检查已测试模式系统以确认其分类的方法，该方法侧重于更古老的特征。在Rovash脚本和图形序列上验证了该方法的有效性。采用PAST4软件进行多变量分析，采用主坐标分析排序和k-means聚类算法。

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Ordination-based verification of feature selection in pattern evolution research

This article explains the idea of pattern systems that develop gradually. These systems involve symbolic communication that includes symbols, syntax, and layout rules. Some pattern systems change over time, like historical scripts. The scientific study of pattern systems is called pattern evolution research, and scriptinformatics is concerned with the modelling of the evolution of scripts. The symbol series consists of symbols from a pattern system, while the graph sequence is a symbol sequence applied with a specific technology. This article describes a method for examining tested pattern systems to confirm their classification, which focuses on more ancient features. The method’s effectiveness was tested on Rovash scripts and graph sequences. Multivariate analysis was carried out by using PAST4 software, employing principal coordinates analysis ordination and k-means clustering algorithms.

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来源期刊

Intelligent Data Analysis 工程技术-计算机：人工智能

CiteScore

2.20

自引率

5.90%

发文量

审稿时长

3.3 months

期刊介绍： Intelligent Data Analysis provides a forum for the examination of issues related to the research and applications of Artificial Intelligence techniques in data analysis across a variety of disciplines. These techniques include (but are not limited to): all areas of data visualization, data pre-processing (fusion, editing, transformation, filtering, sampling), data engineering, database mining techniques, tools and applications, use of domain knowledge in data analysis, big data applications, evolutionary algorithms, machine learning, neural nets, fuzzy logic, statistical pattern recognition, knowledge filtering, and post-processing. In particular, papers are preferred that discuss development of new AI related data analysis architectures, methodologies, and techniques and their applications to various domains.