Probabilistic Multi-Dimensional Classification

TL;DR

This paper introduces a flexible, optimal probabilistic multi-dimensional classification framework that decomposes complex tasks into simpler probabilistic classifiers using a directed acyclic graph, improving accuracy and interpretability.

Contribution

It presents a novel formal framework for probabilistic MDC that addresses accuracy, scalability, data type limitations, interpretability, and uncertainty estimation simultaneously.

Findings

Framework is flexible and provably optimal

Decomposition into smaller probabilistic classifiers improves scalability

Experimental results demonstrate the framework's usefulness

Abstract

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.