Feature selection is a central problem in machine learning and pattern recognition. On large
datasets (in terms of dimension and/or number of instances), using search-based or wrapper …

Many learning problems require handling high dimensional datasets with a relatively small
number of instances. Learning algorithms are thus confronted with the curse of …

An excessive number of features may negatively affect the performance of a learning
classifier. In addition, the computational time for processing the data during the training …

Feature selection plays an important role in pattern classification. It is especially an
important preprocessing task when there are large number of features in comparison to …

Abstract− Microarray Data, often characterised by high-dimensions and small samples, is
used for cancer classification problems that classify the given (tissue) samples as deceased …

Feature selection is an important method to provide both efficiency and effectiveness for
high-dimension data clustering. However, most feature selection methods require prior …

In many applications, we deal with high dimensional datasets with different types of data. For
instance, in text classification and information retrieval problems, we have large collections …

Feature selection is widely used in text clustering to reduce dimensions in the feature space.
In this paper, we study and propose two-stage unsupervised feature selection methods to …

Real-world machine learning datasets may be highly complex. Data of a single class may be
distributed irregularly throughout the feature space and measures of distance as a proxy for …

Understanding the visual system is a real interest since it contributes to one of the most
important sense for human: the vision. To achieve this goal and with a focus on neuronal …