MRMD2.0: A Python Tool for Machine Learning with Feature Ranking and Reduction
MRMD2.0: A Python Tool for Machine Learning with Feature Ranking and Reduction
复制标题
DOI：
10.2174/1574893615999200503030350
复制
发表时间：
2020-01-01
影响因子：
4
通讯作者：
Ding, Hui
中科院分区：
文献类型：
--
作者：
He, Shida;Guo, Fei;Ding, Hui
背景：降维是机器学习过程中的关键问题。目的：使用多种特征选择算法来选择数据特征，实现降维。方法：首先，MRMD2.0结合PageRank策略集成了7种不同的流行特征排序算法。结果：在实验中取得了较好的效果。结论：已有多项工作在MRMD2.0上进行了检验。它表现出了良好的性能。此外，还可以根据特征维度绘制性能曲线。如果用户想要牺牲精度以换取更少的特征，可以从性能曲线中选择维度。其他：我们与Web服务器一起开发了友好的Python工具。用户可以上传他们的csv、arff或libsvm格式的文件。然后，网络服务器将帮助对特征进行排序并找到最优维度。
Aims: The study aims to find a way to reduce the dimensionality of the dataset.Background: Dimensionality reduction is the key issue of the machine learning process. It does not only improve the prediction performance but also could recommend the intrinsic features and help to explore the biological expression of the machine learning "black box".Objective: A variety of feature selection algorithms are used to select data features to achieve dimensionality reduction.Methods: First, MRMD2.0 integrated 7 different popular feature ranking algorithms with PageRank strategy. Second, optimized dimensionality was detected with forward adding strategy.Result: We have achieved good results in our experiments.Conclusion: Several works have been tested with MRMD2.0. It showed well performance. Otherwise, it also can draw the performance curves according to the feature dimensionality. If users want to sacrifice accuracy for fewer features, they can select the dimensionality from the performance curves.Other: We developed friendly python tools together with the web server. The users could upload their csv, arff or libsvm format files. Then the webserver would help to rank features and find the optimized dimensionality.