Iterative classification for multiple target attributes

摘要

Many real-world applications require the simultaneous prediction of multiple target attributes. The techniques currently available for these problems either employ a global model that simultaneously predicts all target attributes or rely on the aggregation of individual models, each predicting one target. This paper introduces a novel solution. Our approach employs an iterative classification strategy to exploit the relationships among multiple target attributes to achieve higher accuracy. The computation scheme is developed as a wrapper in which many standard single-target classification algorithms can be simply “plugged-in” to simultaneously predict multiple targets. An empirical evaluation using eight data sets shows that the proposed method outperforms (1) an approach that constructs independent classifiers for each target, (2) a multitask neural network method, and (3) ensembles of multi-objective decision trees in terms of simultaneously predicting all target attributes correctly.