Maximizing the classification accuracy and minimizing the number of selected features are
two primary objectives in feature selection, which is inherently a multiobjective task …

In recent years, multi-objective optimization (MOO) techniques have become popular due to
their potentiality in solving a wide variety of real-world problems, including bioinformatics …

Feature selection is to reduce both the dimensionality of data and the classification error rate
(ie, increase the classification accuracy) of a learning algorithm. The two objectives are often …

Mining the best logical rule from the data is a challenging task because not all attribute of the
dataset will contribute towards the optimal logical representation. Even if the correct …

Creating optimal logic mining is strongly dependent on how the learning data are structured.
Without optimal data structure, intelligence systems integrated into logic mining, such as an …

In the past few years, educational data mining (EDM) has attracted the attention of
researchers to enhance the quality of education. Predicting student academic performance …

Since the exploration of multiple solution sets will lead to the deterioration of convergence in
multi-objective particle swarm optimization, the motion of the particles is severely disturbed …

Feature selection aims to reduce the number of features and improve the classification
accuracy, which is an essential step in many real-world problems. Multiple feature subsets …

We propose a two-stage surrogate-assisted evolutionary approach to address the
computational issues arising from using Genetic Algorithm (GA) for feature selection in a …

Researchers have considered multi-label learning because of its presence in various real-
world applications, in which each entity is associated with more than one class label. Since …