过度拟合
计算机科学
特征选择
瓶颈
降维
选择(遗传算法)
可扩展性
维数之咒
人工智能
分析
大数据
特征提取
特征(语言学)
机器学习
数据挖掘
数据库
人工神经网络
哲学
嵌入式系统
语言学
作者
B. Venkatesh,J. Anuradha
出处
期刊:Cybernetics and Information Technologies
日期:2019-03-01
卷期号:19 (1): 3-26
被引量:362
标识
DOI:10.2478/cait-2019-0001
摘要
Abstract Nowadays, being in digital era the data generated by various applications are increasing drastically both row-wise and column wise; this creates a bottleneck for analytics and also increases the burden of machine learning algorithms that work for pattern recognition. This cause of dimensionality can be handled through reduction techniques. The Dimensionality Reduction (DR) can be handled in two ways namely Feature Selection (FS) and Feature Extraction (FE). This paper focuses on a survey of feature selection methods, from this extensive survey we can conclude that most of the FS methods use static data. However, after the emergence of IoT and web-based applications, the data are generated dynamically and grow in a fast rate, so it is likely to have noisy data, it also hinders the performance of the algorithm. With the increase in the size of the data set, the scalability of the FS methods becomes jeopardized. So the existing DR algorithms do not address the issues with the dynamic data. Using FS methods not only reduces the burden of the data but also avoids overfitting of the model.
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