Many-Objective Feature Selection (MOFS) approaches use four or more
objectives to determine the relevance of a subset of features in a supervised
learning task. As a consequence, MOFS typically returns a large set of
non-dominated solutions, which have to be assessed by the data scientist in
order to proceed with the final choice. Given the multi-variate nature of the
assessment, which may include criteria (e.g. fairness) not related to
predictive accuracy, this step is often not straightforward and suffers from
the lack of existing tools. For instance, it is common to make use of a tabular
presentation of the solutions, which provide little information about the
trade-offs and the relations between criteria over the set of solutions.
This paper proposes an original methodology to support data scientists in the
interpretation and comparison of the MOFS outcome by combining post-processing
and visualisation of the set of solutions. The methodology supports the data
scientist in the selection of an optimal feature subset by providing her with
high-level information at three different levels: objectives, solutions, and
individual features.
The methodology is experimentally assessed on two feature selection tasks
adopting a GA-based MOFS with six objectives (number of selected features,
balanced accuracy, F1-Score, variance inflation factor, statistical parity, and
equalised odds). The results show the added value of the methodology in the
selection of the final subset of features.

本研究提出了一种原创方法，通过后处理和可视化来支持数据科学家解释和比较 MOFS 结果，从而帮助其选择最佳特征子集。

实现多目标特征选择可解释性的数据科学流程

A data-science pipeline to enable the Interpretability of Many-Objective  Feature Selection

Metal-organic frameworks (MOFs) are of immense interest in applications such
as gas storage and carbon capture due to their exceptional porosity and tunable
chemistry. Their modular nature has enabled the use of template-based methods
to generate hypothetical MOFs by combining molecular building blocks in
accordance with known network topologies. However, the ability of these methods
to identify top-performing MOFs is often hindered by the limited diversity of
the resulting chemical space. In this work, we propose MOFDiff: a
coarse-grained (CG) diffusion model that generates CG MOF structures through a
denoising diffusion process over the coordinates and identities of the building
blocks. The all-atom MOF structure is then determined through a novel assembly
algorithm. Equivariant graph neural networks are used for the diffusion model
to respect the permutational and roto-translational symmetries. We
comprehensively evaluate our model's capability to generate valid and novel MOF
structures and its effectiveness in designing outstanding MOF materials for
carbon capture applications with molecular simulations.

提出了一种基于扩散模型的 CG-MOF 结构生成方法，结合模拟生成了有效且新颖的 MOF 结构，并应用于碳捕获材料的设计。