摘要：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.
