Using the Rasch Model and k-Nearest Neighbors Algorithm for Response Classification

Jon-Paul Paolino

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Using the Rasch Model and k-Nearest Neighbors Algorithm for Response Classification

机译：使用Rasch模型和k最近邻算法进行响应分类

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In this paper we propose using the k-nearest neighbors (k-NN) algorithm (Cover and Hart, 1967) for classifying and predicting the responses to dichotomous items. We show using the percent correct statistic how k-NN can be used with Rasch model parameter estimation methods such as joint maximum likelihood (JMLE), conditional maximum likelihood estimation (CMLE), marginal maximum likelihood estimation (MMLE), and marginal Bayes modal estimation (MBME). We further suggest how one can use the algorithm to predict responses on future assessments. The empirical data set that we used to illustrate this procedure was the fraction subtraction data set from Tatsuoka (1984). Using R software we show the accuracy and efficacy of k-NN for classifying responses.

机译：在本文中，我们提出使用k最近邻（k-NN）算法（Cover和Hart，1967）来分类和预测对二分项的响应。我们使用百分比正确的统计数据展示了如何将k-NN与Rasch模型参数估计方法（例如联合最大似然（JMLE），条件最大似然估计（CMLE），边际最大似然估计（MMLE）和边际贝叶斯模态估计）一起使用（MBME）。我们进一步建议人们如何使用该算法来预测未来评估的响应。我们用来说明此过程的经验数据集是Tatsuoka（1984）的分数减法数据集。使用R软件，我们展示了k-NN用于分类响应的准确性和有效性。

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《Journal of applied measurement》 |2016年第2期|185-193|共9页
作者
Jon-Paul Paolino;
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Mercy College, 63 Cornwells Beach Road, Port Washington, NY 11050, USA;

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正文语种 eng
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Using the Rasch Model and k-Nearest Neighbors Algorithm for Response Classification

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