# MOBA: A multi-objective bounded-abstention model for two-class cost-sensitive problems

## TL;DR

The paper introduces MOBA, a multi-objective abstention model for two-class cost-sensitive problems that optimizes multiple metrics without relying on explicit cost information, using evolutionary algorithms to generate Pareto-optimal solutions.

## Contribution

It proposes a novel multi-objective bounded-abstention model that does not require cost estimation and balances multiple performance metrics using evolutionary optimization.

## Key findings

- MOBA achieves lower expected costs compared to state-of-the-art models.
- It provides better trade-offs between performance and abstention.
- The model is robust to variations in cost information and performance demands.

## Abstract

Abstaining classifiers have been widely used in cost-sensitive applications to avoid ambiguous classification and reduce the cost of misclassification. Previous abstaining classification models rely on cost information, such as a cost matrix or cost ratio. However, it is difficult to obtain or estimate costs in practical applications. Furthermore, these abstention models are typically restricted to a single optimization metric, which may not be the expected indicator when evaluating classification performance. To overcome such problems, a multi-objective bounded-abstention (MOBA) model is proposed to optimize essential metrics. Specifically, the MOBA model minimizes the error rate of each class under class-dependent abstention constraints. The MOBA model is then solved using the non-dominated sorting genetic algorithm II, which is a popular evolutionary multi-objective optimization algorithm. A set of Pareto-optimal solutions will be generated and the best one can be selected according to provided conditions (whether costs are known) or performance demands (e.g., obtaining a high accuracy, F-measure, and etc). Hence, the MOBA model is robust towards variations in the conditions and requirements. Compared to state-of-the-art abstention models, MOBA achieves lower expected costs when cost information is considered, and better performance-abstention trade-offs when it is not.