Abstract
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.
Highlights
Machine learning algorithms have been successfully applied to several knowledge areas, such as speech recognition [1], image recognition [2], pattern discovery [3], word processing [4], the financial market [5], clustering [6], and automated decision support [7]
information visualization techniques (InfoVis) may have an important role in the understanding and analysis of the variety of machine learning models
Evolutionary algorithms (EAs) seek to select the best individuals for each generation using a selection method based on a fitness value, which is a measure of the quality of the candidate solution being represented by an individual
Summary
Machine learning algorithms have been successfully applied to several knowledge areas, such as speech recognition [1], image recognition [2], pattern discovery [3], word processing [4], the financial market [5], clustering [6], and automated decision support [7]. We would improve user confidence and reduce processing time when generating these models It is a current computer science challenge to develop techniques or tools that are able to produce transparent and explainable models and to provide a description of their internal decision-making process [11,12,13]. In this context, information visualization techniques (InfoVis) create images that can help users better understand the data and their relationships.
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