An efficient model-free estimation of multiclass conditional probability
This work addresses the need for efficient multiclass probability estimation in machine learning, offering an incremental improvement over existing methods.
The paper tackles the problem of estimating multiclass conditional probabilities without restrictive distributional assumptions by proposing a model-free method based on conditional quantile regression functions, achieving competitive performance especially with many classes.
Conventional multiclass conditional probability estimation methods, such as Fisher's discriminate analysis and logistic regression, often require restrictive distributional model assumption. In this paper, a model-free estimation method is proposed to estimate multiclass conditional probability through a series of conditional quantile regression functions. Specifically, the conditional class probability is formulated as difference of corresponding cumulative distribution functions, where the cumulative distribution functions can be converted from the estimated conditional quantile regression functions. The proposed estimation method is also efficient as its computation cost does not increase exponentially with the number of classes. The theoretical and numerical studies demonstrate that the proposed estimation method is highly competitive against the existing competitors, especially when the number of classes is relatively large.