Metaheuristic algorithms are widely known as efficient solutions for solving problems of
optimization. These algorithms supply powerful instruments with significant engineering …

At present, there is no unified method for solving multiattribute decision-making problems. In
this paper, we propose two methods that benefit from some novel fuzzy rough set models …

In the domain of artificial neural networks, the learning process represents one of the most
challenging tasks. Since the classification accuracy highly depends on the weights and …

Feature selection is an indispensable work to make the data mining more effective. It
reduces the computational complexity and effectively improves the performance of learning …

Feature selection problem from the domain of machine learning refers to selecting only
those features from the high dimensional datasets, that have prominent influence on …

Feature selection plays a pivotal role in machine learning, serving as a critical
preprocessing step. Its impact extends beyond enhancing the classification capabilities of …

Obtaining efficient optimisation algorithms has become the focus of much research interest
since current developing trends in machine learning, traffic management, and other cutting …

The fast-growing quantity of information hinders the process of machine learning, making it
computationally costly and with substandard results. Feature selection is a pre-processing …

Simulated annealing (SA) was recognized as an effective local search optimizer, and it
showed a great success in many real-world optimization problems. However, it has slow …

Feature subset selection is essential for identifying relevant and non-redundant features,
which enhances classification accuracy and simplifies machine learning models. Given the …