Using SVM to pre-classify government purchases
This addresses a specific issue for government auditing in Brazil by providing a tool to reduce misclassification, though it is incremental as it applies an existing method (SVM) to a new dataset.
The paper tackled the problem of misclassification of goods purchased by the Brazilian government, which hinders expenditure auditing, by training an SVM classifier on 20 million purchases from 1999 to 2015, achieving that the correct category code was among the top three predictions in 83.3% of cases.
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.