Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach

TL;DR

This paper introduces a taxonomy-based feature selection method for trajectory datasets that improves interpretability, reduces computational complexity, and maintains or enhances predictive performance by classifying features into geometric and kinematic categories.

Contribution

The paper proposes a novel taxonomy-based feature selection approach that categorizes features by their internal structure, enhancing interpretability and efficiency in trajectory data analysis.

Findings

Taxonomy-based feature selection achieves comparable or better predictive accuracy.

Reduces feature selection time by limiting the combinatorial search space.

Provides insights into dataset sensitivity to different feature groups.

Abstract

Trajectory analysis is not only about obtaining movement data, but it is also of paramount importance in understanding the pattern in which an object moves through space and time, as well as in predicting its next move. Due to the significant interest in the area, data collection has improved substantially, resulting in a large number of features becoming available for training and predicting models. However, this introduces a high-dimensionality-induced feature explosion problem, which reduces the efficiency and interpretability of the data, thereby reducing the accuracy of machine learning models. To overcome this issue, feature selection has become one of the most prevalent tools. Thus, the objective of this paper was to introduce a taxonomy-based feature selection method that categorizes features based on their internal structure. This approach classifies the data into geometric and kinematic features, further categorizing them into curvature, indentation, speed, and acceleration. The comparative analysis indicated that a taxonomy-based approach consistently achieved comparable or superior predictive performance. Furthermore, due to the taxonomic grouping, which reduces combinatorial space, the time taken to select features was drastically reduced. The taxonomy was also used to gain insights into what feature sets each dataset was more sensitive to. Overall, this study provides robust evidence that a taxonomy-based feature selection method can add a layer of interpretability, reduce dimensionality and computational complexity, and contribute to high-level decision-making. It serves as a step toward providing a methodological framework for researchers and practitioners dealing with trajectory datasets and contributing to the broader field of explainable artificial intelligence.