Visual Analysis Scenarios for Understanding Evolutionary Computational Techniques’ Behavior

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.

Keywords

• information visualization
• machine learning
• evolutionary algorithms
• clustering algorithms