Metaheuristic algorithms on feature selection: A survey of one decade of research (2009-2019)
Feature selection is a critical and prominent task in machine learning. To reduce the
dimension of the feature set while maintaining the accuracy of the performance is the main …
dimension of the feature set while maintaining the accuracy of the performance is the main …
[HTML][HTML] Multiclass feature selection with metaheuristic optimization algorithms: a review
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 …
selection is harder to perform since most classifications are binary. The feature selection …
Stock market prediction using machine learning classifiers and social media, news
Accurate stock market prediction is of great interest to investors; however, stock markets are
driven by volatile factors such as microblogs and news that make it hard to predict stock …
driven by volatile factors such as microblogs and news that make it hard to predict stock …
[HTML][HTML] Swarm intelligence algorithms for feature selection: a review
Featured Application The paper analyzes the usage and mechanisms of feature selection
methods that are based on swarm intelligence in different application areas. Abstract The …
methods that are based on swarm intelligence in different application areas. Abstract The …
[HTML][HTML] A new firefly algorithm with improved global exploration and convergence with application to engineering optimization
Firefly algorithm (FA) is a powerful and efficient meta-heuristic algorithm which has shown
effective performance in the recent literature when applied to solving engineering …
effective performance in the recent literature when applied to solving engineering …
[HTML][HTML] A hybrid binary dwarf mongoose optimization algorithm with simulated annealing for feature selection on high dimensional multi-class datasets
The dwarf mongoose optimization (DMO) algorithm developed in 2022 was applied to solve
continuous mechanical engineering design problems with a considerable balance of the …
continuous mechanical engineering design problems with a considerable balance of the …
Feature selection for image steganalysis using levy flight-based grey wolf optimization
Image steganalysis is the process of detecting the availability of hidden messages in the
cover images. Therefore, it may be considered as a classification problem which categorizes …
cover images. Therefore, it may be considered as a classification problem which categorizes …
Firefly algorithm and its variants in digital image processing: A comprehensive review
The significance and requirements of digital image processing arise from two main areas of
applications: the improvement of visual information for human interpretation and the …
applications: the improvement of visual information for human interpretation and the …
Deep learning for real-time image steganalysis: a survey
F Ruan, X Zhang, D Zhu, Z Xu, S Wan, L Qi - Journal of Real-Time Image …, 2020 - Springer
Steganography is a technique that transmits secret data or message in an appropriate
multimedia carrier, eg, image, audio, and video files. It comes under the assumption that if …
multimedia carrier, eg, image, audio, and video files. It comes under the assumption that if …
Steganalysis feature selection with multidimensional evaluation & dynamic threshold allocation
Y Ma, L Xu, Y Zhang, T Zhang… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Steganalysis feature selection shows excellent effectiveness on elevating the detection
efficiency and decreasing time-space cost. However, the single evaluation criterion for …
efficiency and decreasing time-space cost. However, the single evaluation criterion for …