Model-agnostic local explanation: Multi-objective genetic algorithm explainer

IF 7.5 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Engineering Applications of Artificial Intelligence Pub Date : 2024-11-20 DOI:10.1016/j.engappai.2024.109628
Hossein Nematzadeh , José García-Nieto , Sandro Hurtado , José F. Aldana-Montes , Ismael Navas-Delgado
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Abstract

Late detection of plant diseases leads to irreparable losses for farmers, threatening global food security, economic stability, and environmental sustainability. This research introduces the Multi-Objective Genetic Algorithm Explainer (MOGAE), a novel model-agnostic local explainer for image data aimed at the early detection of citrus diseases. MOGAE enhances eXplainable Artificial Intelligence (XAI) by leveraging the Non-dominated Sorting Genetic Algorithm II (NSGA-II) with an adaptive Bit Flip Mutation (BFM) incorporating densify and sparsify operators to adjust superpixel granularity automatically. This innovative approach simplifies the explanation process by eliminating several critical hyperparameters required by traditional methods like Local Interpretable Model-Agnostic Explanations (LIME). To develop the citrus disease classification model, we preprocess the leaf dataset through stratified data splitting, oversampling, and augmentation techniques, then fine-tuning a pre-trained Residual Network 50 layers (ResNet50) model. MOGAE’s effectiveness is demonstrated through comparative analyses with the Ensemble-based Genetic Algorithm Explainer (EGAE) and LIME, showing superior accuracy and interpretability using criteria such as numeric accuracy of explanation and Number of Function Evaluations (NFE). We assess accuracy both intuitively and numerically by measuring the Euclidean distance between expert-provided explanations and those generated by the explainer. The appendix also includes an extensive evaluation of MOGAE on the melanoma dataset, highlighting its versatility and robustness in other domains. The related implementation code for the fine-tuned ResNet50 and MOGAE is available at https://github.com/KhaosResearch/Plant-disease-explanation.

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与模型无关的本地解释:多目标遗传算法解释器
植物病害检测不及时会给农民造成无法弥补的损失,威胁全球粮食安全、经济稳定和环境可持续性。本研究介绍了多目标遗传算法解释器(MOGAE),这是一种新颖的图像数据局部解释器,用于早期检测柑橘病害。MOGAE 利用非优势排序遗传算法 II(NSGA-II)和自适应比特翻转突变(BFM),结合致密化和稀疏化算子,自动调整超像素粒度,从而增强了可解释人工智能(XAI)。这种创新方法消除了本地可解释模型诊断解释(LIME)等传统方法所需的几个关键超参数,从而简化了解释过程。为了开发柑橘病害分类模型,我们通过分层数据分割、超采样和增强技术对叶片数据集进行了预处理,然后对预先训练好的 50 层残差网络(ResNet50)模型进行了微调。通过与基于集合的遗传算法解释器(EGAE)和 LIME 的比较分析,我们证明了 MOGAE 的有效性,并根据解释的数字准确性和函数评估次数(NFE)等标准,展示了其卓越的准确性和可解释性。我们通过测量专家提供的解释与解释器生成的解释之间的欧氏距离,从直观和数值两方面评估了准确性。附录还包括在黑色素瘤数据集上对 MOGAE 的广泛评估,突出了它在其他领域的通用性和鲁棒性。微调后的 ResNet50 和 MOGAE 的相关实现代码请访问 https://github.com/KhaosResearch/Plant-disease-explanation。
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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