通过进化多目标神经架构搜索设计新型认知诊断模型
Designing Novel Cognitive Diagnosis Models via Evolutionary Multi-Objective Neural Architecture Search
AI总结：
本文提出用进化多目标神经架构搜索自动设计认知诊断模型，通过多目标遗传编程同时优化性能与可解释性，实验证明其优于现有模型。
摘要：
认知诊断在现代智能教育平台中起着至关重要的作用，用于揭示学生在知识概念上的掌握程度，以支持后续的自适应任务。然而，由于对模型可解释性的高要求，现有的人工设计认知诊断模型结构过于简单，难以满足当前智能教育系统的需求，同时人工设计的偏差也限制了有效认知诊断模型的出现。在本文中，我们提出通过进化多目标神经架构搜索（NAS）自动设计新型认知诊断模型。具体来说，我们观察到现有模型可以由一个处理三类给定输入的通用模型来表示，因此首先为认知诊断中的NAS任务设计了一个具有表达能力的搜索空间。然后，我们提出多目标遗传编程（MOGP），通过最大化模型性能和可解释性来探索NAS任务的搜索空间。在MOGP设计中，每个架构被转换为树形架构并用树进行编码以便于优化，同时设计了一种基于四个子遗传操作的定制遗传操作来有效生成后代。此外，还提出了一种初始化策略，通过从现有模型变体中进化一半种群来加速收敛。在两个真实世界数据集上的实验表明，所提方法搜索到的认知诊断模型在性能上显著优于现有模型，并且具有与人工设计模型相当的可解释性。
英文摘要：
Cognitive diagnosis plays a vital role in modern intelligent education platforms to reveal students' proficiency in knowledge concepts for subsequent adaptive tasks. However, due to the requirement of high model interpretability, existing manually designed cognitive diagnosis models hold too simple architectures to meet the demand of current intelligent education systems, where the bias of human design also limits the emergence of effective cognitive diagnosis models. In this paper, we propose to automatically design novel cognitive diagnosis models by evolutionary multi-objective neural architecture search (NAS). Specifically, we observe existing models can be represented by a general model handling three given types of inputs and thus first design an expressive search space for the NAS task in cognitive diagnosis. Then, we propose multi-objective genetic programming (MOGP) to explore the NAS task's search space by maximizing model performance and interpretability. In the MOGP design, each architecture is transformed into a tree architecture and encoded by a tree for easy optimization, and a tailored genetic operation based on four sub-genetic operations is devised to generate offspring effectively. Besides, an initialization strategy is also suggested to accelerate the convergence by evolving half of the population from existing models' variants. Experiments on two real-world datasets demonstrate that the cognitive diagnosis models searched by the proposed approach exhibit significantly better performance than existing models and also hold as good interpretability as human-designed models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
