Effective detection of organizations is essential for fighting crime and
maintaining public safety, especially considering the limited human resources
and tools to deal with each group that exhibits co-movement patterns. This
paper focuses on solving the Network Structure Inference (NSI) challenge. Thus,
we introduce two new approaches to detect network structure inferences based on
agent trajectories. The first approach is based on the evaluation of graph
entropy, while the second considers the quality of clustering indices. To
evaluate the effectiveness of the new approaches, we conducted experiments
using four scenario simulations based on the animal kingdom, available on the
NetLogo platform: Ants, Wolf Sheep Predation, Flocking, and Ant Adaptation.
Furthermore, we compare the results obtained with those of an approach
previously proposed in the literature, applying all methods to simulations of
the NetLogo platform. The results demonstrate that our new detection approaches
can more clearly identify the inferences of organizations or networks in the
simulated scenarios.

通过基于代理轨迹的图熵评估和聚类指标质量考虑，该研究提出了两种新的方法来检测网络结构推理，实验证明这些方法可以更清晰地识别模拟场景中的组织或网络的推理。

通过轨迹检测群体中的组织证据

Detecting Evidence of Organization in groups by Trajectories

Classification model selection is a process of identifying a suitable model
class for a given classification task on a dataset. Traditionally, model
selection is based on cross-validation, meta-learning, and user preferences,
which are often time-consuming and resource-intensive. The performance of any
machine learning classification task depends on the choice of the model class,
the learning algorithm, and the dataset's characteristics. Our work proposes a
novel method for automatic classification model selection from a set of
candidate model classes by determining the empirical model-fitness for a
dataset based only on its clustering indices. Clustering Indices measure the
ability of a clustering algorithm to induce good quality neighborhoods with
similar data characteristics. We propose a regression task for a given model
class, where the clustering indices of a given dataset form the features and
the dependent variable represents the expected classification performance. We
compute the dataset clustering indices and directly predict the expected
classification performance using the learned regressor for each candidate model
class to recommend a suitable model class for dataset classification. We
evaluate our model selection method through cross-validation with 60 publicly
available binary class datasets and show that our top3 model recommendation is
accurate for over 45 of 60 datasets. We also propose an end-to-end Automated ML
system for data classification based on our model selection method. We evaluate
our end-to-end system against popular commercial and noncommercial Automated ML
systems using a different collection of 25 public domain binary class datasets.
We show that the proposed system outperforms other methods with an excellent
average rank of 1.68.

本文提出了一种基于数据集聚类指数的方法来实现自动化的分类模型选择，通过回归任务计算特征和依赖变量，精准预测预期的分类性能来推荐合适的模型类别，同时设计了一种结合该模型选择方法的全自动化数据分类系统，在大规模的数据集实验中得出了优于商业和非商业自动化系统的性能表现。