This paper investigates the impact of feature encoding techniques on the
explainability of XAI (Explainable Artificial Intelligence) algorithms. Using a
malware classification dataset, we trained an XGBoost model and compared the
performance of two feature encoding methods: Label Encoding (LE) and One Hot
Encoding (OHE). Our findings reveal a marginal performance loss when using OHE
instead of LE. However, the more detailed explanations provided by OHE
compensated for this loss. We observed that OHE enables deeper exploration of
details in both global and local contexts, facilitating more comprehensive
answers. Additionally, we observed that using OHE resulted in smaller
explanation files and reduced analysis time for human analysts. These findings
emphasize the significance of considering feature encoding techniques in XAI
research and suggest potential for further exploration by incorporating
additional encoding methods and innovative visualization approaches.

研究探究特征编码技术对可解释人工智能算法的影响，发现在恶意软件分类数据集中，使用 One Hot Encoding（OHE）方法相对于使用 Label Encoding（LE）方法，虽然性能略差，但它提供了更详细的解释，深入探索全局和局部上下文的细节，方便提供更全面的答案。此外，使用 OHE 的说明文件更小，分析人员的分析时间也更少。

特征编码对恶意软件分类可解释性的影响

Impact of Feature Encoding on Malware Classification Explainability

In this paper we propose a new intermediate supervision method, named
LabelEnc, to boost the training of object detection systems. The key idea is to
introduce a novel label encoding function, mapping the ground-truth labels into
latent embedding, acting as an auxiliary intermediate supervision to the
detection backbone during training. Our approach mainly involves a two-step
training procedure. First, we optimize the label encoding function via an
AutoEncoder defined in the label space, approximating the "desired"
intermediate representations for the target object detector. Second, taking
advantage of the learned label encoding function, we introduce a new auxiliary
loss attached to the detection backbones, thus benefiting the performance of
the derived detector. Experiments show our method improves a variety of
detection systems by around 2% on COCO dataset, no matter one-stage or
two-stage frameworks. Moreover, the auxiliary structures only exist during
training, i.e. it is completely cost-free in inference time. Code is available
at: this https URL

通过引入标签编码函数及后向传导方法，本文提出了一种名为 LabelEnc 的新型中间半监督方法来提高物体检测系统的训练效果，并在 COCO 数据集上进行了实验，结果表明，本方法可以提高各种检测系统的检测性能达到 2%，在推理时间上本方法没有额外的成本。

LabelEnc：一种新的目标检测中间监督方法

LabelEnc: A New Intermediate Supervision Method for Object Detection

One of the most prominent problems in machine learning in the age of deep
learning is the availability of sufficiently large annotated datasets. For
specific domains, e.g. animal species, a long-tail distribution means that some
classes are observed and annotated insufficiently. Additional labels can be
prohibitively expensive, e.g. because domain experts need to be involved.
However, there is more information available that is to the best of our
knowledge not exploited accordingly. In this paper, we propose to make use of
preexisting class hierarchies like WordNet to integrate additional domain
knowledge into classification. We encode the properties of such a class
hierarchy into a probabilistic model. From there, we derive a novel label
encoding and a corresponding loss function. On the ImageNet and NABirds
datasets our method offers a relative improvement of 10.4% and 9.6% in accuracy
over the baseline respectively. After less than a third of training time, it is
already able to match the baseline's fine-grained recognition performance. Both
results show that our suggested method is efficient and effective.

基于 WordNet 类层次结构的机器学习方法提高了 ImageNet 和 NABirds 数据集的分类准确度，并在较短的训练时间内成功匹配了基线模型的细粒度识别性能。