LOFS: Library of Online Streaming Feature Selection
원문 초록
As an emerging research direction, online streaming feature selection deals with sequentially added dimensions in a feature space while the number of data instances is fixed. Online streaming feature selection provides a new, complementary algorithmic methodology to enrich online feature selection, especially targets to high dimensionality in big data analytics. This paper introduces the first comprehensive open-source library for use in MATLAB that implements the state-of-the-art algorithms of online streaming feature selection. The library is designed to facilitate the development of new algorithms in this exciting research direction and make comparisons between the new methods and existing ones available.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy
Simple is what you need for efficient and accurate medical image segmentation
Permutation-Free High-Order Interaction Tests
Scalable unsupervised feature selection via weight stability
FailureSensorIQ: A Multi-Choice QA Dataset for Understanding Sensor Relationships and Failure Modes
토론
이 논문과 연결된 포럼 글이 아직 없습니다.