文章基本信息

标题：Visual Analysis Scenarios for Understanding Evolutionary Computational Techniques’ Behavior
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作者：Aruanda Meiguins ; Aruanda Meiguins ; Yuri Santos 等
期刊名称：Information
电子版ISSN：2078-2489
出版年度：2019
卷号：10
期号：3
页码：88
DOI：10.3390/info10030088
出版社：MDPI Publishing
摘要：Machine learning algorithms are used in many applications nowadays. Sometimes, we need to describe how the decision models created output, and this may not be an easy task. Information visualization (InfoVis) techniques (e.g., TreeMap, parallel coordinates, etc.) can be used for creating scenarios that visually describe the behavior of those models. Thus, InfoVis scenarios were used to analyze the evolutionary process of a tool named AutoClustering, which generates density-based clustering algorithms automatically for a given dataset using the EDA (estimation-of-distribution algorithm) evolutionary technique. Some scenarios were about fitness and population evolution (clustering algorithms) over time, algorithm parameters, the occurrence of the individual, and others. The analysis of those scenarios could lead to the development of better parameters for the AutoClustering tool and algorithms and thus have a direct impact on the processing time and quality of the generated algorithms.
关键词：information visualization; machine learning; evolutionary algorithms; clustering algorithms information visualization ; machine learning ; evolutionary algorithms ; clustering algorithms