In recent years, Jaya optimization algorithm has been successfully applied in several
optimization problems. This paper presents a novel feature selection (FS) approach based …

In order to improve the classification accuracy, a preprocessing step is used to pre-filter
some redundant or irrelevant features before decision tree construction. And a new feature …

Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

The use of feature selection can improve accuracy, efficiency, applicability and
understandability of a learning process and its resulting model. For this reason, many …

One of the challenges in data mining is the dimensionality of data, which is often very high
and prevalent in many domains, such as text categorization and bio-informatics. The high …

In this paper, the efficacy of seven data classification methods; Decision Tree (DT), k-
Nearest Neighbor (k-NN), Logistic Regression (LogR), Naïve Bayes (NB), C4. 5, Support …

Feature selection and data discretization are two important data pre-processing steps in
data mining, with the focus in the former being on filtering out unrepresentative features and …

It is known that Naive Bayesian classifier (NB) works very well on some domains, and poorly
on others. The performance of NB suffers in domains that involve correlated features. C4. 5 …