Selecting relevant feature subsets is vital in machine learning, and multiclass feature
selection is harder to perform since most classifications are binary. The feature selection …

Breast cancer (BC) is one of the leading causes of high mortality rates among women. An
early disease diagnosis is crucial in breast cancer's treatment for improving the survival rate …

The high-dimensional features in the data may affect the performance of the classification
model as all of them are not useful. The selection of relevant optimal features is a tedious …

In smart factories, the data collected by Internet-of-things sensors is enormous and includes
a lot of noise and missing values. To address this big data problem, metaheuristic is one of …

Breast cancer is still a major problem for medical research, science, and society. Breast
cancer is the most common form of cancer among women and has a high rate of mortality …

The dimensions of microarray datasets are very large, containing noise and redundancy.
The problem with microarray datasets is the presence of more features compared to the …

Accurate diagnosis and effective treatment of thyroid conditions, such as hypothyroidism and
hyperthyroidism, are crucial due to their wide-ranging symptoms and consequences …

Fire is one of the most harmful hazards that affect daily life. The existing fire detection
methods have the problems of large computation, slow detection speed, and low detection …

Microarray datasets have very high dimensions and contain noise and redundancy. The
issue with the microarray dataset is that it includes more characteristics than the samples …

Feature selection is both important and difficult part in classification technology. It is used to
reduce the dimensionality of dataset and remove unwanted features. In this paper, we are …