Sequential Classification on Lattices with Experiment-Specific Response Distributions
Sequential Classification on Lattices with Experiment-Specific Response Distributions
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DOI：
10.1080/07474946.2014.916931
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发表时间：
2014-01-01
影响因子：
0.8
通讯作者：
Tatsuoka, Curtis
中科院分区：
文献类型：
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作者：
Tatsuoka, Curtis
描述了有限格子模型上的贝叶斯序贯分类框架，其中允许响应分布随实验而变化。建立了分类的最优收敛速度。提出了直观、计算简单的实验选择规则，并证明了这类规则在一般条件下几乎必然达到最优率。仿真研究表明，该方法可以在保持较高分类精度的情况下，有效地对格进行顺序分类，极大地节省了实验管理时间。该框架可用于认知评估的适应性测试，以及当实验反应分布依赖于池组成时的其他顺序分类问题，如群体测试。
Bayesian framework for sequential classification on finite lattice models is described in which response distributions are allowed to vary according to experiment. Optimal rates of convergence in classification are established. Intuitive and computationally simple experiment selection rules are proposed, and it is shown that this class of rules attains optimal rates almost surely under general conditions. A simulation study demonstrates that sequential classification can be conducted efficiently on lattices, with potentially great savings in experiment adminstration while maintaining high classification accuracy. This framework can be applied to adaptive testing for cognitive assessment and to other sequential classification problems such as group testing when experimental response distributions depend on pool composition.