et al. Feature selection: A data perspective[J]. ACM Computing Surveys (CSUR), 2017, 50(6):...
Feature selection, as a data preprocessing strategy, has been proven to be effective and efficient in preparing data (especially high-dimensional data) for various data-mining and machine-learning problems. The objectives of feature selection include building simpler and more comprehensible models, impro...
Feature Selection: A Data Perspective --阅读笔记1 特征选择的概述,程序员大本营,技术文章内容聚合第一站。
S. Alelyan,"On Feature Selection Stability: A Data Perspective", PhD dissertation, Arizona State University, Tempe, AZ, USA, 2013.Alelyan, S. On feature selection stability: A data perspective. PhD dissertation, Arizona State Univ., Tempe, AZ, USA, 2013....
Subspace, Latent Structure and Feature Selection: Statistical and Optimization Perspectives Workshop Feature Extraction, Construction and Selection: A Data Mining Perspective 特征选择是特征工程的一个子课题. 你可能会喜欢深入看一下特征工程的话题. “Discover Feature Engineering, How to Engineer Features and ...
Moreover, UFSSOD, as an online capable algorithm, yields comparable results to a state-of-the-art offline method trimmed for outlier detection. Keywords: feature selection; outlier detection; intrusion detection; network security; machine learning; online learning; unsupervised learning; streaming data...
@article{li2018feature, title={Feature selection: A data perspective}, author={Li, Jundong and Cheng, Kewei and Wang, Suhang and Morstatter, Fred and Trevino, Robert P and Tang, Jiliang and Liu, Huan}, journal={ACM Computing Surveys (CSUR)}, volume={50}, number={6}, pages={94},...
In the context of high-dimensional credit card fraud data, researchers and practitioners commonly utilize feature selection techniques to enhance the performance of fraud detection models. This study presents a comparison in model performance using the m
High-dimensional data analysis is a challenge for researchers and engineers in the fields of machine learning and data mining. Feature selection provides an effective way to solve this problem by removing irrelevant and redundant data, which can reduce computation time, improve learning accuracy, and...
It is possible to automatically select those features in your data that are most useful or most relevant for the problem you are working on. This is a process called feature selection. In this post you will discover feature selection, the types of methods that you can use and a handy check...