Assistive Guideline of Categorization for Competitive Programming Problems

Abstract

Programming is a very useful skill nowadays. Programming contests give people the opportunity to increase their programming skills. By solving programming contest problems contestants can increase not only their programming skills but also their mathematical and algorithmic knowledge. The competitive programming problems are presented in problem statements. Sometimes they are presented in the form of a story or sometimes directly. To solve the problem contestants must read the problem statement carefully. The problems can be of many categories. We have tried to classify number theory and graph theory problems. At first, we collected data from competitive programming problem statements. Then we used different machine learning algorithms such as fully connected neural network, naive bayes classifier, support vector machine on the data to predict if the category of the problem is either number theory or graph theory. With such machine learning approaches we achieved test accuracy of about 72%, 75% and 74%.