Intellectual property in colombian museums: an application of machine learning

Abstract

The purpose of this research is to answer the following guiding question: how can the behavior of museum networks in Colombia be predicted with respect to the protection of intellectual property (copyright, confidential information and use of patents, domain names, industrial designs, use of trademarks) and the interaction of different types of proximity (geographical, organizational, relational, cognitive, cultural and institutional), based on the use of supervised learning algorithms? Among the main findings are that the best learning algorithms to predict the behavior of networks, considering different target variables are the AdaBoost, the naive Bayes and CN2 rule inducer