Author:
AlNuaimi Noura,Masud Mohammad Mehedy,Serhani Mohamed Adel,Zaki Nazar
Abstract
Organizations in many domains generate a considerable amount of heterogeneous data every day. Such data can be processed to enhance these organizations’ decisions in real time. However, storing and processing large and varied datasets (known as big data) is challenging to do in real time. In machine learning, streaming feature selection has always been considered a superior technique for selecting the relevant subset features from highly dimensional data and thus reducing learning complexity. In the relevant literature, streaming feature selection refers to the features that arrive consecutively over time; despite a lack of exact figure on the number of features, numbers of instances are well-established. Many scholars in the field have proposed streaming-feature-selection algorithms in attempts to find the proper solution to this problem. This paper presents an exhaustive and methodological introduction of these techniques. This study provides a review of the traditional feature-selection algorithms and then scrutinizes the current algorithms that use streaming feature selection to determine their strengths and weaknesses. The survey also sheds light on the ongoing challenges in big-data research.
Subject
Computer Science Applications,Information Systems,Software
Reference83 articles.
1. 6 Predictions For The $203 Billion Big Data Analytics Market;Gil Press;Forbes
2. Irrelevant features and the subset selection problem;others,1994
3. Efficient feature selection via analysis of relevance and redundancy;J. Mach. Learn. Res.,2004
4. A fast scoring algorithm for maximum likelihood estimation in unbalanced mixed models with nested random effects;Biometrika,1987
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