Tagging Fireworkers Activities from Body Sensors under Distribution Drift
Citation: Proceedings of the 2015 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 5, pages 389–396 (2015)
Abstract. We describe our submission to the AAIA'15 Data Mining Competition, where the objective is to tag the activity of firefighters based on vital functions and movement sensor readings. Our solution exploits a selective naive Bayes classifier, with optimal preprocessing, variable selection and model aver- aging, together with an automatic variable construction method that builds many variables from time series records. The most challenging part of the challenge is that the input variables are not independent and identically distributed (i.i.d.) between the train and test datasets. We suggest a methodology to alleviate this problem, that enabled to get a final score of 0.76 (team marcb).