Summary of Feature Selection Via Robust Weighted Score For High Dimensional Binary Class-imbalanced Gene Expression Data, by Zardad Khan et al.
Feature Selection via Robust Weighted Score for High Dimensional Binary Class-Imbalanced Gene Expression Data
by Zardad Khan, Amjad Ali, Saeed Aldahmani
First submitted to arxiv on: 23 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel weighted score, called ROWSU, is introduced to tackle the class-imbalance problem in high-dimensional gene expression binary classification. This approach aims to select the most discriminative features despite skewed class distributions. The method involves balancing the training dataset by generating minority class data points, followed by a greedy search for selecting a minimum subset of genes and a weighted robust score computed using support vectors. The proposed ROWSU method combines the highest-scoring genes with the minimum subset selected via greedy search to form the final set of genes. The approach is evaluated on six gene expression datasets, utilizing classification accuracy and sensitivity as performance metrics, outperforming existing feature selection procedures based on k nearest neighbours (kNN) and random forest (RF) classifiers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists create a new way to choose the most important genes in a dataset. This is done by first making the data more balanced, then selecting the most useful genes. The new method works well even when there are many more observations of one type than another. The team tested their approach on six different datasets and found it performed better than other methods. |
Keywords
* Artificial intelligence * Classification * Feature selection * Random forest
