# Adaptive Learning for the Resource-Constrained Classification Problem

arXiv: https://arxiv.org/abs/2207.09196 · HTML: https://mlsanity.com/paper/2207.09196/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 48 references that are in the map.
- First posted on arXiv 19 Jul 2022.

## Builds on (most cited first, 6 of 6)

- [Class-Balanced Loss Based on Effective Number of Samples](https://mlsanity.com/paper/1901.05555/) (2019)
- [Machine Learning for Combinatorial Optimization: a Methodological Tour d'Horizon](https://mlsanity.com/paper/1811.06128/) (2018)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Smart "Predict, then Optimize"](https://mlsanity.com/paper/1710.08005/) (2017)
- [Optimization under Uncertainty in the Era of Big Data and Deep Learning: When Machine Learning Meets Mathematical Programming](https://mlsanity.com/paper/1904.01934/) (2019)
- [Profit Driven Decision Trees for Churn Prediction](https://mlsanity.com/paper/1712.08101/) (2017)

## Built on by (most cited first, 1 of 1)

- [Graph-Based Automatic Feature Selection for Multi-Class Classification via Mean Simplified Silhouette](https://mlsanity.com/paper/2309.02272/) (2023)

## Abstract (arXiv)

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.