Curse of dimensionality is known as big challenges in data mining, pattern recognition,
computer vison and machine learning in recent years. Feature selection and feature …

Data from many real-world applications can be high dimensional and features of such data
are usually highly redundant. Identifying informative features has become an important step …

In the past decades, the rapid growth of computer and database technologies has led to the
rapid growth of large-scale medical datasets. On the other, medical applications with high …

Feature selection has been widely used in data mining and machine learning tasks to make
a model with a small number of features which improves the classifier's accuracy. In this …

Feature selection is a combinatorial optimization problem that selects most relevant features
from an original feature set to increase the performance of classification or clustering …

In this paper, we proposed a novel spam detection method that focused on reducing the
false positive error of mislabeling nonspam as spam. First, we used the wrapper-based …

This paper investigates the feature subset selection problem for the binary classification
problem using logistic regression model. We developed a modified discrete particle swarm …

Abstract Crow Search Algorithm (CSA) is a simple yet effective meta-heuristic algorithm that
has been applied to solve many engineering problems. In CSA, fl parameter controls the …

To effectively predict stock price for investors is a very important research problem. In
literature, data mining techniques have been applied to stock (market) prediction. Feature …

Feature subset selection (FSS) has received a great deal of attention in statistics, machine
learning, and data mining. Real world data analyzed by data mining algorithms can involve …