Conditional random fields (CRFs) have been touted to be some of the most powerful methods and algorithms that underpin machine learning, says Professor Rao Kotagiri at University of Melbourne’s Department of Computing and Information Systems.
CRFs are a type of probabilistic graphical model in machine learning, and can be applied to computer vision, natural language processing and bioinformatics. Machine learning is where a computer is programmed to pick up on patterns in example or past data to predict future outcomes or train itself on how to best respond in certain situations.
The aim is to label something, given certain features, Kotagiri said. For example, a bank might be trying to determine if a prospective customer will be ‘low’ or ‘high’ risk for a loan, which is the label (y). To do that, the bank has to evaluate the person’s income, current debt situation and so on, which are the features (x).
“In the case of conditional random fields, what we are trying to do is we want to represent the probability of a sequence of labelling, given the observations. We want to find the best sequence of labels by looking at all the features that we have,” Kotagiri told attendees at the recent Big Data Summit held at the University of Technology, Sydney.
Generally, CRFs can be mathematically written as: