Building an Ensemble from a Single Naive Bayes Classifier in the Analysis of Key Risk Factors for Polish State Fire Service

Abstract

In this paper, we describe our solution in a competition that required performing data mining to identify key risk factors for the State Fire Service of Poland. The goal was to create an ensemble of Naive Bayes classifiers that could predict incidents involving firefighters, rescuers, children, or civilians. To this end, we first created a single Naive Bayes classifier and then partitioned the set of attributes used in that classifier. The attribute subsets were used to create new Naive Bayes classifiers that would form an ensemble, which generally performs better than both the single classifier and ensemble obtained by searching over all attributes considered when creating the single classifier. The application of our approach yielded a solution that ranked third in the competition.