Minimum redundancy feature selection is an algorithm frequently used in a method to accurately identify characteristics of genes and phenotypes and narrow down their relevance and is …

2021年2月12日 · “Maximum Relevance — Minimum Redundancy” (aka MRMR) is an algorithm used by Uber’s machine learning platform for finding the “minimal-optimal” subset of features.

mRMR, which stands for "minimum Redundancy - Maximum Relevance", is a feature selection algorithm. The peculiarity of mRMR is that it is a minimal-optimal feature selection algorithm. …

MRMR is an iterative algorithm. At each iteration, it determines the mean redundancy between the remaining features and the features that were selected in previous rounds. With the …

2019年9月2日 · 绪论在特征选择过程中，有一种算法叫做mRMR（Max-Relevance and Min-Redundancy）。 其原理非常简单，就是在原始特征集合中找到与最终输出结果相关性最 …

2024年1月1日 · In summary, the MRMR algorithm serves as a powerful tool in feature selection, balancing relevance and redundancy to enhance classification models’ performance. Its …

In this paper, a filter method called mRMR is studied and evaluated to be implemented in an automated machine learning platform. The advantage of the filter method is the computation …

2019年8月15日 · This paper describes the approach to extend, evaluate, and implement the mRMR feature selection methods for classification problem in a marketing machine learning …

2018年12月19日 · In this paper, we propose two new versions of Mrmr and Cfs that output the same feature set as the original algorithms, but are considerably much faster. Our novel …

For mutual information based feature selection methods like this web-version of mRMR, you might want to discretize your own data first as a few categorical states, -- empirically this leads to …