Balancing peatlands fire data using ANS-SMOTE method for improvement prediction of peatlands fire occurrence

It is known that the studies of peatlands fire occurrences in Indonesia are less studied before. In our previous study, the prediction of the peatlands fire occurrence was modeled using various machine learning classification approaches. It is found that using South Kalimantan Province data, in the empirical study we previously found that the datasets are unbalanced, i.e., the occurrence and the nonoccurrence of fire hotspots areas. In the study presented in this paper, to improve the classification performance, we consider Adaptive Neighbor Synthetic Majority Oversampling Technique (ANS-SMOTE) approach to balance the data. Using the considered empirical data, we found that this method did not always gives improvement in the classification results.