Prediction of forest fire using hybrid fuzzy-clustering - Bagging method

Various classification methods have been considered to predict the occurrence of the forest fire, including the recent ensemble methods, such as bootstrap aggregating (bagging) method and its extension. Here we consider a hybrid approach between fuzzy c-means clustering and bagging. As the weak learner in the bagging approach, we consider multinomial logistic (multilogit) regression and Support Vector Machines (SVM) classification approaches. To see their empirical performance, the proposed approaches are applied to the public data set, where the bagging approach has not been considered as the classification method in the other studies yet. From the empirical results, we obtain the hybrid fuzzy c-means clustering - the bagging multilogit regression approach has the best accuracy for classifying the size of forest fire data.