Explainable Artificial Intelligence (XAI) has mainly focused on static
learning scenarios so far. We are interested in dynamic scenarios where data is
sampled progressively, and learning is done in an incremental rather than a
batch mode. We seek efficient incremental algorithms for computing feature
importance (FI) measures, specifically, an incremental FI measure based on
feature marginalization of absent features similar to permutation feature
importance (PFI). We propose an efficient, model-agnostic algorithm called iPFI
to estimate this measure incrementally and under dynamic modeling conditions
including concept drift. We prove theoretical guarantees on the approximation
quality in terms of expectation and variance. To validate our theoretical
findings and the efficacy of our approaches compared to traditional batch PFI,
we conduct multiple experimental studies on benchmark data with and without
concept drift.

提出了一种名为 iPFI 的有效的、模型无关的算法，用于在动态建模条件下估计特征边际化的增量特征重要性评估方法，包括概念漂移。实验证明，相比传统的批处理 PFI 方法，该方法具有良好的效果。

增量置换特征重要性（iPFI）：面向数据流的在线解释

Incremental Permutation Feature Importance (iPFI): Towards Online Explanations on Data Streams

There has been a significant effort by the research community to address the
problem of providing methods to organize documentation with the help of
information Retrieval methods. In this report paper, we present several
experiments with some stream analysis methods to explore streams of text
documents. We use only dynamic algorithms to explore, analyze, and organize the
flux of text documents. This document shows a case study with developed
architectures of a Text Document Stream Organization, using incremental
algorithms like Incremental TextRank, and IS-TFIDF. Both these algorithms are
based on the assumption that the mapping of text documents and their
document-term matrix in lower-dimensional evolving networks provides faster
processing when compared to batch algorithms. With this architecture, and by
using FastText Embedding to retrieve similarity between documents, we compare
methods with large text datasets and ground truth evaluation of clustering
capacities. The datasets used were Reuters and COVID-19 emotions. The results
provide a new view for the contextualization of similarity when approaching
flux of documents organization tasks, based on the similarity between documents
in the flux, and by using mentioned algorithms.

本论文通过使用增量算法如 Incremental TextRank 和 IS-TFIDF 来探索文本文档流，并构建了一种文本文档流组织的体系结构，以便更快速地处理大规模文本数据集的聚类能力，并基于文档之间的相似性提供了一种全新的文档组织视角。

文本文件流组织的情境化

Contextualization for the Organization of Text Documents Streams

The semina\"ive algorithm can materialise all consequences of arbitrary
datalog rules, and it also forms the basis for incremental algorithms that
update a materialisation as the input facts change. Certain (combinations of)
rules, however, can be handled much more efficiently using custom algorithms.
To integrate such algorithms into a general reasoning approach that can handle
arbitrary rules, we propose a modular framework for materialisation computation
and its maintenance. We split a datalog program into modules that can be
handled using specialised algorithms, and handle the remaining rules using the
semina\"ive algorithm. We also present two algorithms for computing the
transitive and the symmetric-transitive closure of a relation that can be used
within our framework. Finally, we show empirically that our framework can
handle arbitrary datalog programs while outperforming existing approaches,
often by orders of magnitude.

本文提出一种基于模块化框架的材料化计算和维护的方法，该方法使用半朴素算法处理少数规则，同时可使用两种算法计算关系的传递闭包，实验表明，与现有的方法相比，本方法可以处理任意的 datalog 程序，而且性能也要高得多。