# FDive: Learning Relevance Models using Pattern-based Similarity Measures

## TL;DR

FDive is a visual active learning system that creates relevance models using pattern-based similarity measures, aiding analysts in exploring high-dimensional data and improving classification accuracy through user feedback.

## Contribution

We introduce FDive, a novel system combining pattern-based similarity ranking with interactive relevance modeling and active learning for high-dimensional data analysis.

## Key findings

- FDive outperforms state-of-the-art feature selection techniques.
- It improves the quality and interpretability of relevance models.
- Demonstrated effectiveness in classifying brain cell electron microscopy images.

## Abstract

The detection of interesting patterns in large high-dimensional datasets is difficult because of their dimensionality and pattern complexity. Therefore, analysts require automated support for the extraction of relevant patterns. In this paper, we present FDive, a visual active learning system that helps to create visually explorable relevance models, assisted by learning a pattern-based similarity. We use a small set of user-provided labels to rank similarity measures, consisting of feature descriptor and distance function combinations, by their ability to distinguish relevant from irrelevant data. Based on the best-ranked similarity measure, the system calculates an interactive Self-Organizing Map-based relevance model, which classifies data according to the cluster affiliation. It also automatically prompts further relevance feedback to improve its accuracy. Uncertain areas, especially near the decision boundaries, are highlighted and can be refined by the user. We evaluate our approach by comparison to state-of-the-art feature selection techniques and demonstrate the usefulness of our approach by a case study classifying electron microscopy images of brain cells. The results show that FDive enhances both the quality and understanding of relevance models and can thus lead to new insights for brain research.