Due to the presence of irrelevant or redundant data in microarray datasets, capturing
potential patterns accurately and directly via existing models is difficult. Feature selection …

In the real business situation, suppliers usually provide retailers with forward financing to
decrease inventory or increase demand. Moreover, some heterogeneous goods are not …

Learning a classifier from an imbalanced dataset is an important problem in data mining and
machine learning. Since there is more information from the majority classes than the …

Due to the efficiency and efficacy in performance to tackle complex optimization problems,
swarm intelligence (SI) optimizers, newly emerged as nature-inspired algorithms, have …

Bio and natural phenomena inspired algorithms and meta-heuristics provide solutions to
solve optimization and preliminary convergence problems. It significantly has wide effect that …

Due to the high-dimensional characteristics of dataset, we propose a new method based on
the Wolf Search Algorithm (WSA) for optimising the feature selection problem. The proposed …

Emerging as one of the most contemporary machine learning techniques, deep learning has
shown success in areas such as image classification, speech recognition, and even playing …

Background: There have been rapid developments in various bioinformatics technologies,
which have led to the accumulation of a large amount of biomedical data. However, these …

Optimizing hydropower plants is complex due to nonlinearity, complexity, and
multidimensionality. This study introduces and evaluates the performance of the Wolf Search …

Imbalanced datasets can be found in a number of fields; they are commonly regarded as big
data because of their sheer volume and high attribute dimensions. As the name suggests …