Choosing a journal to publish a work is a task that involves many variables. Usually, the authors’ experience allows them to classify journals into categories, according to their suitability and the characteristics of the article. However, there are certain aspects in the choice that are probabilistic in nature, whose modelling may provide some help. Suppose an author has to choose a journal from a preference list to publish an article. The researcher is interested in publishing the paper in a journal with a rank number less than or equal to k . For this purpose, a simple classification model is presented in order to choose the best journal from the list, from which some fundamental consequences can be deduced and simple rules derived. For example, if the list contains 100 journals and is ordered using 2-year impact factor, the rule “send to the journal at the k − 10 position” is adequate.

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我应该在哪里提交我的作品以供出版？优化选择的非对称分类模型

选择期刊发表作品是一项涉及很多变数的任务。通常，作者的经验使他们能够根据期刊的适用性和文章的特点将期刊分类。但是，选择中的某些方面本质上是概率性的，其建模可能会提供一些帮助。假设作者必须从偏好列表中选择期刊才能发表文章。研究人员有兴趣在排名数小于或等于 k ​​的期刊上发表论文。为此，提出了一个简单的分类模型，以便从列表中选择最好的期刊，从中可以推断出一些基本的后果并推导出简单的规则。例如，如果列表包含 100 种期刊并使用 2 年影响因子排序，