Deep ensemble network with explicit complementary model for accuracy-balanced classification
This work addresses the need for more balanced classification performance across categories, which is an incremental improvement for machine learning applications requiring fairness or reliability.
The paper tackles the problem of reducing accuracy deviation among categories in classification systems without degrading overall average accuracy, and presents Harmony, an ensemble-like deep neural network that achieves this goal.
The average accuracy is one of major evaluation metrics for classification systems, while the accuracy deviation is another important performance metric used to evaluate various deep neural networks. In this paper, we present a new ensemble-like fast deep neural network, Harmony, that can reduce the accuracy deviation among categories without degrading overall average accuracy. Harmony consists of three sub-models, namely, Target model, Complementary model, and Conductor model. In Harmony, an object is classified by using either Target model or Complementary model. Target model is a conventional classification network for general categories, while Complementary model is a classification network especially for weak categories that are inaccurately classified by Target model. Conductor model is used to select one of two models. Experimental results demonstrate that Harmony accurately classifies categories, while it reduces the accuracy deviation among the categories.