Probabilistic Multi-Dimensional Classification
This addresses accuracy, scalability, and interpretability issues in multi-dimensional classification for applications requiring multiple class predictions.
The paper tackles the limitations of existing multi-dimensional classification methods by proposing a formal probabilistic framework that decomposes learning into single-variable classifiers and a directed acyclic graph, achieving provable optimality and flexibility.
Multi-dimensional classification (MDC) can be employed in a range of applications where one needs to predict multiple class variables for each given instance. Many existing MDC methods suffer from at least one of inaccuracy, scalability, limited use to certain types of data, hardness of interpretation or lack of probabilistic (uncertainty) estimations. This paper is an attempt to address all these disadvantages simultaneously. We propose a formal framework for probabilistic MDC in which learning an optimal multi-dimensional classifier can be decomposed, without loss of generality, into learning a set of (smaller) single-variable multi-class probabilistic classifiers and a directed acyclic graph. Current and future developments of both probabilistic classification and graphical model learning can directly enhance our framework, which is flexible and provably optimal. A collection of experiments is conducted to highlight the usefulness of this MDC framework.