A Gamified Approach to Naïve Bayes Classification: A Case Study for Newswires and Systematic Medical Reviews

Abstract

Supervised machine learning algorithms require a set of labelled examples to be trained; however, the labelling process is a costly and time consuming task which is carried out by experts of the domain who label the dataset by means of an iterative process to filter out non-relevant objects of the dataset. In this paper, we describe a set of experiments that use gamification techniques to transform this labelling task into an interactive learning process where users can cooperate in order to achieve a common goal. To this end, first we use a geometrical interpretation of Naïve Bayes (NB) classifiers in order to create an intuitive visualization of the current state of the system and let the user change some of the parameters directly as part of a game. We apply this visualization technique to the classification of newswire and we report the results of the experiments conducted with different groups of people: PhD students, Master Degree students and general public. Then, we present a preliminary experiment of query rewriting for systematic reviews in a medical scenario, which makes use of gamification techniques to collect different formulation of the same query. Both the experiments show how the exploitation of gamification approaches help to engage the users in abstract tasks that might be hard to understand and/or boring to perform.