Feature Selection for Regression Problems Based on the Morisita Estimator of Intrinsic Dimension
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.
Code (0)
등록된 구현이 없습니다.
Tasks
feature selectionManagementregressionSimilar Papers 제목 키워드 기반
Unsupervised Feature Selection Based on the Morisita Estimator of Intrinsic Dimension
This paper deals with a new filter algorithm for selecting the smallest subset of features carrying all the information content of a data set (i.e. for removing redundant features). It is an advanced version of the fract…
Dimensionality Reductionfeature selectionRobust Variable Selection and Estimation Via Adaptive Elastic Net S-Estimators for Linear Regression
Heavy-tailed error distributions and predictors with anomalous values are ubiquitous in high-dimensional regression problems and can seriously jeopardize the validity of statistical analyses if not properly addressed. Fo…
regressionVariable SelectionFAStEN: An Efficient Adaptive Method for Feature Selection and Estimation in High-Dimensional Functional Regressions
Functional regression analysis is an established tool for many contemporary scientific applications. Regression problems involving large and complex data sets are ubiquitous, and feature selection is crucial for avoiding…
CPUfeature selectionparameter estimationregressionPredictability Analysis of Regression Problems via Conditional Entropy Estimations
In the field of machine learning, regression problems are pivotal due to their ability to predict continuous outcomes. Traditional error metrics like mean squared error, mean absolute error, and coefficient of determinat…
regressionFactor Importance Ranking and Selection using Total Indices
Factor importance measures the impact of each feature on output prediction accuracy. Many existing works focus on the model-based importance, but an important feature in one learning algorithm may hold little significanc…
Binary Classification