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 …

Due to sharp increases in data dimensions, working on every data mining or machine
learning (ML) task requires more efficient techniques to get the desired results. Therefore, in …

The rapid advance of computer based high-throughput technique have provided
unparalleled opportunities for humans to expand capabilities in production, services …

Feature selection is a pre-processing step, used to improve the mining performance by
reducing data dimensionality. Even though there exists a number of feature selection …

As basic research, it has also received increasing attention from people that the “curse of
dimensionality” will lead to increase the cost of data storage and computing; it also …

Feature selection, as a dimensionality reduction technique, aims to choosing a small subset
of the relevant features from the original features by removing irrelevant, redundant or noisy …

Feature selection is a robust technique for data reduction and an essential step in successful
machine learning applications. Different feature selection methods have been introduced in …

Abstract Recent advancements in Information Technology (IT) have engendered the rapid
production of big data, as enormous volumes of data with high dimensional features grow …

Dimensionality reduction as a preprocessing step to machine learning is effective in
removing irrelevant and redundant data, increasing learning accuracy, and improving result …

Feature selection (FS) is an important research topic in the area of data mining and machine
learning. FS aims at dealing with the high dimensionality problem. It is the process of …