使用SVM对政府采购进行预分类
Using SVM to pre-classify government purchases
摘要：
巴西政府经常对购买的商品进行错误分类。这使得审计政府支出变得困难。如果一支圆珠笔（代码#7510）被错误地归类为技术绘图笔（代码#6675）或任何其他商品，我们无法知道该笔的价格是否合理。本文展示了我们如何使用机器学习来减少错误分类。我训练了一个支持向量机（SVM）分类器，它以产品描述为输入，并返回最可能的类别代码作为输出。我使用巴西政府在1999-04-01和2015-04-02之间购买的2000万种商品来训练分类器。在83.3%的情况下，正确的类别代码是由分类器识别的三个最有可能的类别代码之一。我使用经过训练的分类器开发了一个网络应用程序，可以帮助政府减少错误分类。我在GitHub上开源了代码;任何人都可以使用和修改它。
英文摘要：
The Brazilian government often misclassifies the goods it buys. That makes it hard to audit government expenditures. We cannot know whether the price paid for a ballpoint pen (code #7510) was reasonable if the pen was misclassified as a technical drawing pen (code #6675) or as any other good. This paper shows how we can use machine learning to reduce misclassification. I trained a support vector machine (SVM) classifier that takes a product description as input and returns the most likely category codes as output. I trained the classifier using 20 million goods purchased by the Brazilian government between 1999-04-01 and 2015-04-02. In 83.3% of the cases the correct category code was one of the three most likely category codes identified by the classifier. I used the trained classifier to develop a web app that might help the government reduce misclassification. I open sourced the code on GitHub; anyone can use and modify it.
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。
