Machine learning (ML) has employed various discretization methods to
partition numerical attributes into intervals. However, an effective
discretization technique remains elusive in many ML applications, such as
association rule mining. Moreover, the existing discretization techniques do
not reflect best the impact of the independent numerical factor on the
dependent numerical target factor. This research aims to establish a benchmark
approach for numerical attribute partitioning. We conduct an extensive analysis
of human perceptions of partitioning a numerical attribute and compare these
perceptions with the results obtained from our two proposed measures. We also
examine the perceptions of experts in data science, statistics, and engineering
by employing numerical data visualization techniques. The analysis of collected
responses reveals that $68.7\%$ of human responses approximately closely align
with the values generated by our proposed measures. Based on these findings,
our proposed measures may be used as one of the methods for discretizing the
numerical attributes.

本研究通过收集人类和数据科学、统计学以及工程专家的反馈，分析了数值属性的分割，提出了两种度量方法，结果显示约 68.7% 的人类反馈与我们的度量方法一致，因此我们的度量方法可作为一种用于数值属性离散化的方法之一。

数值属性的离散化：人类感知的分析

Discretizing Numerical Attributes: An Analysis of Human Perceptions

Spectral clustering and its extensions usually consist of two steps: (1)
constructing a graph and computing the relaxed solution; (2) discretizing
relaxed solutions. Although the former has been extensively investigated, the
discretization techniques are mainly heuristic methods, e.g., k-means, spectral
rotation. Unfortunately, the goal of the existing methods is not to find a
discrete solution that minimizes the original objective. In other words, the
primary drawback is the neglect of the original objective when computing the
discrete solution. Inspired by the first-order optimization algorithms, we
propose to develop a first-order term to bridge the original problem and
discretization algorithm, which is the first non-heuristic to the best of our
knowledge. Since the non-heuristic method is aware of the original graph cut
problem, the final discrete solution is more reliable and achieves the
preferable loss value. We also theoretically show that the continuous optimum
is beneficial to discretization algorithms though simply finding its closest
discrete solution is an existing heuristic algorithm which is also unreliable.
Sufficient experiments significantly show the superiority of our method.

基于一阶优化算法，我们提出了第一个非启发式方法，将原始问题与离散化算法联系起来，从而更可靠地获得最终的离散解决方案，优于现有方法。

通过非启发式算法离散化谱聚类的松弛解

Discretize Relaxed Solution of Spectral Clustering via a Non-Heuristic  Algorithm

IoT device identification is the process of recognizing and verifying
connected IoT devices to the network. This is an essential process for ensuring
that only authorized devices can access the network, and it is necessary for
network management and maintenance. In recent years, machine learning models
have been used widely for automating the process of identifying devices in the
network. However, these models are vulnerable to adversarial attacks that can
compromise their accuracy and effectiveness. To better secure device
identification models, discretization techniques enable reduction in the
sensitivity of machine learning models to adversarial attacks contributing to
the stability and reliability of the model. On the other hand, Ensemble methods
combine multiple heterogeneous models to reduce the impact of remaining noise
or errors in the model. Therefore, in this paper, we integrate discretization
techniques and ensemble methods and examine it on model robustness against
adversarial attacks. In other words, we propose a discretization-based ensemble
stacking technique to improve the security of our ML models. We evaluate the
performance of different ML-based IoT device identification models against
white box and black box attacks using a real-world dataset comprised of network
traffic from 28 IoT devices. We demonstrate that the proposed method enables
robustness to the models for IoT device identification.

通过集成离散化技术和集成方法，我们提出了一种基于离散化的集成堆叠技术，以提高我们的机器学习模型的安全性，通过对 28 个物联网设备网络流量的真实数据集进行白盒和黑盒攻击的性能评估，我们证明了提出的方法使得物联网设备识别模型具有鲁棒性。