Developing intelligent analytical tools requires pre-processing data and finding relevant
features that best reinforce the performance of the predictive algorithms. Feature selection …

Both feature selection and hyperparameter tuning are key tasks in machine learning.
Hyperparameter tuning is often useful to increase model performance, while feature …

In classification, feature selection mainly aims at reducing the dataset dimensionality and
increasing the classification accuracy, which also results in higher computational efficiency …

Acceleration data for activity recognition typically are collected on battery-powered devices,
leading to a trade-off between high-accuracy recognition and energy-efficient operation. We …

Feature selection has two main objectives which are to maximise the classification accuracy
and to minimise the number of selected features. Unfortunately, the two objectives are …

Antimicrobial peptides (AMPs) are a promising alternative to small-molecules-based
antibiotics. These peptides are part of most living organisms' innate defense system. In order …

The objectives and requirements of computer science are constantly changing. Nowadays,
when developing an algorithm, it is not enough to solve the problem itself since the energy …

En el sector educativo, la predicción de la deserción de estudiantes es un tema abierto y
que implica el uso de múltiples características relativas al desempeño de los estudiantes …

Feature subset selection is used as a common technique in data pre-processing for pattern
recognition, machine learning and data mining has attracted much attention in recent years …