Adaptive boosting on linear networks

초록

Classification is a supervised machine learning method that predicts a categorical response variable using several explanatory variables. If observations are sampled from a spatial point process, then we can also use x-and y-coordinates as explanatory variables. If the observations are sampled from a known linear network instead of whole space, then the distance between two points is defined in a different manner, and we require a classifier for the linearly clustered data. In this study, we address the classification problem on a tree-shaped linear network. We select a point on the edges in the given linear network to split the space, and then construct a decision tree through recursive splits. We propose an adaptive boosting algorithm using this decision tree as a weak classifier. Finally, we provide some simulated examples and real data analysis, comparing with adaptive boosting based on decision trees constructed using Cartesian coordinates. The proposed method has better accuracy than the comparison method, when the observations are clustered on linear network.

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