Adaptive Learning for the Resource-Constrained Classification Problem

TL;DR

This paper introduces an adaptive learning method that jointly considers resource constraints and classification learning, significantly improving performance in resource-limited scenarios like disease testing and defect detection.

Contribution

It proposes a novel adaptive learning approach that iteratively adjusts misclassification costs to better handle resource constraints during classification.

Findings

Significantly outperforms alternative methods on a public dataset.

Particularly effective for difficult classification problems.

Enhances the toolkit for resource-constrained classification tasks.

Abstract

Resource-constrained classification tasks are common in real-world applications such as allocating tests for disease diagnosis, hiring decisions when filling a limited number of positions, and defect detection in manufacturing settings under a limited inspection budget. Typical classification algorithms treat the learning process and the resource constraints as two separate and sequential tasks. Here we design an adaptive learning approach that considers resource constraints and learning jointly by iteratively fine-tuning misclassification costs. Via a structured experimental study using a publicly available data set, we evaluate a decision tree classifier that utilizes the proposed approach. The adaptive learning approach performs significantly better than alternative approaches, especially for difficult classification problems in which the performance of common approaches may be unsatisfactory. We envision the adaptive learning approach as an important addition to the repertoire of techniques for handling resource-constrained classification problems.