基于内禀维数Morisita估计的回归问题特征选择
Feature Selection for Regression Problems Based on the Morisita Estimator of Intrinsic Dimension
摘要：
数据采集、存储和管理得到了改善，但许多现象的关键因素还不清楚。因此，不相关和冗余的特征人为地增加了数据集的大小，这使得学习任务变得复杂，例如回归。为了解决这个问题，已经提出了特征选择方法。本文提出了一种新的监督滤波器的基础上的Morisita估计的内在维数。它可以识别相关特征，区分冗余和不相关的信息。此外，它提供了一个清晰的图形表示的结果，它可以很容易地实现在不同的编程语言。利用不同复杂程度、样本量和噪声的模拟数据集进行了综合数值实验。建议的算法也成功地测试了现实世界中的应用程序的选择，并与RReliefF使用极端学习机。此外，提出并讨论了一种新的特征相关性度量方法。
英文摘要：
Data acquisition, storage and management have been improved, while the key factors of many phenomena are not well known. Consequently, irrelevant and redundant features artificially increase the size of datasets, which complicates learning tasks, such as regression. To address this problem, feature selection methods have been proposed. This paper introduces a new supervised filter based on the Morisita estimator of intrinsic dimension. It can identify relevant features and distinguish between redundant and irrelevant information. Besides, it offers a clear graphical representation of the results, and it can be easily implemented in different programming languages. Comprehensive numerical experiments are conducted using simulated datasets characterized by different levels of complexity, sample size and noise. The suggested algorithm is also successfully tested on a selection of real world applications and compared with RReliefF using extreme learning machine. In addition, a new measure of feature relevance is presented and discussed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
