mr2PSO: A maximum relevance minimum redundancy feature selection method based on swarm intelligence for support vector machine classification
This paper presents a hybrid filter–wrapper feature subset selection algorithm based on
particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter …
particle swarm optimization (PSO) for support vector machine (SVM) classification. The filter …
Tabu search
Over the last 15 years, hundreds of papers presenting applications of tabu search, a
heuristic method originally proposed by (1986), to various combinatorial problems have …
heuristic method originally proposed by (1986), to various combinatorial problems have …
Bat algorithm and cuckoo search: a tutorial
XS Yang - … , evolutionary computing and metaheuristics: in the …, 2013 - Springer
Nature-inspired metaheuristic algorithms have attracted much attention in the last decade,
and new algorithms have emerged almost every year with a vast, ever-expanding literature …
and new algorithms have emerged almost every year with a vast, ever-expanding literature …
RETRACTED ARTICLE: Feature selection for machine learning classification problems: a recent overview
SB Kotsiantis - Artificial intelligence review, 2014 - Springer
RETRACTED ARTICLE: Feature selection for machine learning classification problems: a
recent overview | Artificial Intelligence Review Skip to main content SpringerLink Account Menu …
recent overview | Artificial Intelligence Review Skip to main content SpringerLink Account Menu …
Design and implementation of a smart home system using multisensor data fusion technology
This paper aims to develop a multisensor data fusion technology-based smart home system
by integrating wearable intelligent technology, artificial intelligence, and sensor fusion …
by integrating wearable intelligent technology, artificial intelligence, and sensor fusion …
TAGA: Tabu Asexual Genetic Algorithm embedded in a filter/filter feature selection approach for high-dimensional data
Feature selection is the process of selecting an optimal subset of features required for
maintaining or improving the performance of data mining models. Recently, hybrid …
maintaining or improving the performance of data mining models. Recently, hybrid …
A wearable inertial pedestrian navigation system with quaternion-based extended Kalman filter for pedestrian localization
YL Hsu, JS Wang, CW Chang - IEEE Sensors Journal, 2017 - ieeexplore.ieee.org
This paper presents a wearable inertial pedestrian navigation system and its associated
pedestrian trajectory reconstruction algorithm for reconstructing pedestrian walking …
pedestrian trajectory reconstruction algorithm for reconstructing pedestrian walking …
Novel tabu learning neuron model with variable activation gradient and its application to secure healthcare
Currently, the latest advances in artificial neural networks have deeply affected various
aspects of the general public. To this end, a new Tabu Learning Neuron (TLN) model with …
aspects of the general public. To this end, a new Tabu Learning Neuron (TLN) model with …
An improved binary dandelion algorithm using sine cosine operator and restart strategy for feature selection
J Dong, X Li, Y Zhao, J Ji, S Li, H Chen - Expert Systems with Applications, 2024 - Elsevier
Feature selection (FS) is an important data preprocessing technology for machine learning
and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection …
and data mining. Metaheuristic algorithm (MH) has been widely used in feature selection …
Electricity market price spike analysis by a hybrid data model and feature selection technique
In a competitive electricity market, energy price forecasting is an important activity for both
suppliers and consumers. For this reason, many techniques have been proposed to predict …
suppliers and consumers. For this reason, many techniques have been proposed to predict …