# Cost-Sensitive Feature Selection by Optimizing F-Measures

arXiv: https://arxiv.org/abs/1904.02301 · HTML: https://mlsanity.com/paper/1904.02301/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Apr 2019.

## Built on by (most cited first, 7 of 7)

- [Inverse Image Frequency for Long-tailed Image Recognition](https://mlsanity.com/paper/2209.04861/) (2022)
- [Filter Methods for Feature Selection in Supervised Machine Learning Applications -- Review and Benchmark](https://mlsanity.com/paper/2111.12140/) (2021)
- [Feature Selection for Imbalanced Data with Deep Sparse Autoencoders Ensemble](https://mlsanity.com/paper/2103.11678/) (2021)
- [Dynamic feature selection in medical predictive monitoring by reinforcement learning](https://mlsanity.com/paper/2405.19729/) (2024)
- [Towards Interpretable and Efficient Feature Selection in Trajectory Datasets: A Taxonomic Approach](https://mlsanity.com/paper/2506.20359/) (2025)
- [Enabling Fine-Grained Operating Points for Black-Box LLMs](https://mlsanity.com/paper/2510.17727/) (2025)
- [Integrating Feature Understandability into Feature Selection for Human-Centred Tabular Explanations](https://mlsanity.com/paper/2610.16627/) (2026)

## Abstract (arXiv)

Feature selection is beneficial for improving the performance of general machine learning tasks by extracting an informative subset from the high-dimensional features. Conventional feature selection methods usually ignore the class imbalance problem, thus the selected features will be biased towards the majority class. Considering that F-measure is a more reasonable performance measure than accuracy for imbalanced data, this paper presents an effective feature selection algorithm that explores the class imbalance issue by optimizing F-measures. Since F-measure optimization can be decomposed into a series of cost-sensitive classification problems, we investigate the cost-sensitive feature selection by generating and assigning different costs to each class with rigorous theory guidance. After solving a series of cost-sensitive feature selection problems, features corresponding to the best F-measure will be selected. In this way, the selected features will fully represent the properties of all classes. Experimental results on popular benchmarks and challenging real-world data sets demonstrate the significance of cost-sensitive feature selection for the imbalanced data setting and validate the effectiveness of the proposed method.