Abstract Machine learning algorithms have successfully entered industry through many real-
world applications (eg, search engines and product recommendations). In these …

As a commonly used technique in data preprocessing, feature selection selects a subset of
informative attributes or variables to build models describing data. By removing redundant …

This paper describes a fast, non-parametric algorithm for feature ranking and selection for
better classification accuracy. In real world cases, some of the features are noisy or …

Feature selection helps to focus resources on relevant dimensions of input data. Usually,
reducing the input dimensionality to the most informative features also simplifies subsequent …

Irrelevant feature elimination, when used correctly, aids in enhancing the feature selection
accuracy which is critical in dimensionality reduction task. The additional intelligence …

Feature selection plays a fundamental role in many data mining and machine learning tasks.
In this paper, we proposed a novel feature selection method, namely, Dynamic Feature …

With rapid development of information technology, dimensionality of data in many
applications is getting higher and higher. However, many features in the high-dimensional …

Feature selection algorithms look to effectively and efficiently find an optimal subset of
relevant features in the data. As the number of features and the data size increases, new …