摘要
由一名新闻记者-机器人与机器学习每日新闻的工作人员新闻编辑-研究人员详细介绍了人工智能中的新数据。根据NewsRx记者在科罗拉多州丹佛的新闻报道，研究表明：“本文提出了一个专家增强的监督特征选择的综合框架，解决了可解释人工智能(XAI)的预处理、内处理和后处理问题。作为预处理XAI的一部分，我们通过信息融合(PSGIF)算法引入概率解生成器，利用集成技术增强遗传算法(GA)的探索和开发能力。”新闻记者从科罗拉多丹佛大学的研究中获得了一句话，“平衡可解释性和预测准确性，”我们建立了两个多目标优化模型,使专家(s)能够指定一个最大可接受的牺牲百分比。这种方法通过减少所选特征的数量并优先考虑那些从领域专家的角度被认为更相关的特征来增强可解释性。这种贡献与处理中的XAI一致。将专家意见作为多目标问题引入到特征选择过程中，传统的特征选择技术考虑到以可显性为中心的目标函数，使得其能够有效地搜索解空间，为此，我们利用了一种强大的元启发式遗传算法(GA)，通过贝叶斯优化对其参数进行优化，在后处理XAI时，我们提出了后验EnSemble算法(PEA)，评估特征的预测能力。PEA能够在客观和主观的重要性之间进行细微的比较，识别出被低估、高估或适当评定的特征。我们在16个公开可用的数据集上评估了我们建议的GAs的性能，重点是在单一目标环境下预测离子的准确性。此外，我们在分类数据集上测试了我们的多目标模型，以显示我们框架的适用性和有效性。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News-Researchers detail new data in Artificial Intelli gence. According to news reporting from Denver, Colorado, by NewsRx journalists, research stated, "This paper presents a comprehensive framework for expert-augm ented supervised feature selection, addressing pre-processing, in-processing, an d postprocessing aspects of Explainable Artificial Intelligence (XAI). As part of pre-processing XAI, we introduce the Probabilistic Solution Generator through the Information Fusion (PSGIF) algorithm, leveraging ensemble techniques to enh ance the exploration and exploitation capabilities of a Genetic Algorithm (GA)." The news correspondents obtained a quote from the research from the University o f Colorado Denver, "Balancing explainability and prediction accuracy, we formula te two multi -objective optimization models that empower expert(s) to specify a maximum acceptable sacrifice percentage. This approach enhances explainability b y reducing the number of selected features and prioritizing those considered mor e relevant from the domain expert ' s perspective. This contribution aligns with in-processing XAI, incorporating expert opinions into the feature selection pro cess as a multi -objective problem. Traditional feature selection techniques lac k the capability to efficiently search the solution space considering our explai nability-focused objective function. To overcome this, we leverage the Genetic A lgorithm (GA), a powerful metaheuristic algorithm, optimizing its parameters thr ough Bayesian optimization. For post-processing XAI, we present the Posterior En semble Algorithm (PEA), estimating the predictive power of features. PEA enables a nuanced comparison between objective and subjective importance, identifying f eatures as underrated, overrated, or appropriately rated. We evaluate the perfor mance of our proposed GAs on 16 publicly available datasets, focusing on predict ion accuracy in a single objective setting. Moreover, we test our multi -objecti ve model on a classification dataset to show the applicability and effectiveness of our framework."
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