A multi-objective evolutionary algorithm with interval based initialization and self-adaptive crossover operator for large-scale feature selection in classification
Feature selection (FS) is an important data pre-processing technique in classification. In
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …
most cases, FS can improve classification accuracy and reduce feature dimension, so it can …
Patient no-show prediction: a systematic literature review
D Carreras-García, D Delgado-Gómez… - Entropy, 2020 - mdpi.com
Nowadays, across the most important problems faced by health centers are those caused by
the existence of patients who do not attend their appointments. Among others, these patients …
the existence of patients who do not attend their appointments. Among others, these patients …
Advanced computational methods for agriculture machinery movement optimization with applications in sugarcane production
M Filip, T Zoubek, R Bumbalek, P Cerny, CE Batista… - Agriculture, 2020 - mdpi.com
This paper considers the evolution of processes applied in agriculture for field operations
developed from non-organized handmade activities into very specialized and organized …
developed from non-organized handmade activities into very specialized and organized …
Opposition-based sine cosine optimizer utilizing refraction learning and variable neighborhood search for feature selection
This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for
the Feature Selection (FS) problem. FS is an essential machine learning and data mining …
the Feature Selection (FS) problem. FS is an essential machine learning and data mining …
An efficient slime mould algorithm combined with k-nearest neighbor for medical classification tasks
Growing science and medical technologies have produced a massive amount of knowledge
on different scales of biological systems. By processing various amounts of medical data …
on different scales of biological systems. By processing various amounts of medical data …
Feature selection by using chaotic cuckoo optimization algorithm with levy flight, opposition-based learning and disruption operator
Feature selection, which plays an important role in high-dimensional data analysis, is
drawing increasing attention recently. Finding the most relevant and important features for …
drawing increasing attention recently. Finding the most relevant and important features for …
Multi-objective feature selection based on quasi-oppositional based Jaya algorithm for microarray data
Feature selection is the highly sought-after pre-processing technique for microarray data
classification. Microarray data is a phenomenally imbalanced, high-dimensional dataset …
classification. Microarray data is a phenomenally imbalanced, high-dimensional dataset …
MOAEOSCA: an enhanced multi-objective hybrid artificial ecosystem-based optimization with sine cosine algorithm for feature selection in botnet detection in IoT
The number of Internet of Things (IoT) devices overgrows, and this technology dominates.
The importance of IoT security and the growing need to devise intrusion detection systems …
The importance of IoT security and the growing need to devise intrusion detection systems …
[HTML][HTML] Machine learning-based prediction models for patients no-show in online outpatient appointments
With the development of information and communication technologies, all public tertiary
hospitals in China began to use online outpatient appointment systems. However, the …
hospitals in China began to use online outpatient appointment systems. However, the …
Detection of anomaly intrusion utilizing self-adaptive grasshopper optimization algorithm
AK Shukla - Neural Computing and Applications, 2021 - Springer
Due to continued growth in both cyberattacks and network data size, organizations need to
develop advanced ways to keep their networks and data secure the dynamic nature of …
develop advanced ways to keep their networks and data secure the dynamic nature of …