Counterfactual explanations (CFE) are methods that explain a machine learning model by giving an alternate class prediction of a data point with some minimal changes in its features. It helps the users to identify their data attributes that caused an undesirable prediction like a loan or credit card rejection. We describe an efficient and an actionable counterfactual (CF) generation method based on particle swarm optimization (PSO). We propose a simple objective function for the optimization of the instance-centric CF generation problem. The PSO brings in a lot of flexibility in terms of carrying out multi-objective optimization in large dimensions, capability for multiple CF generation, and setting box constraints or immutability of data attributes. An algorithm is proposed that incorporates these features and it enables greater control over the proximity and sparsity properties over the generated CFs. The proposed algorithm is evaluated with a set of action-ability metrics in real-world datasets, and the results were superior compared to that of the state-of-the-arts.

翻译：反事实解释（CFE）是一种通过数据点特征的最小化改变给出替代类别预测来解释机器学习模型的方法。它帮助用户识别导致不良预测（如贷款或信用卡申请被拒）的数据属性。我们描述了一种基于粒子群优化（PSO）的高效且具可操作性的反事实（CF）生成方法。针对以实例为中心的反事实生成问题，我们提出了一个简洁的目标函数用于优化。粒子群优化在大维度的多目标优化、多反事实生成能力以及设置数据属性的边界约束或不可变性方面带来了极大的灵活性。本文提出了一种融合这些特征的算法，能够对生成的反事实的近邻性和稀疏性属性进行更强力的控制。该算法在真实数据集上通过一组可操作性指标进行了评估，其结果表明该方法优于当前最先进的技术。