Titlebook: Evolutionary Machine Learning Techniques; Algorithms and Appli Seyedali Mirjalili,Hossam Faris,Ibrahim Aljarah Book 2020 Springer Nature Si

micturition

Ige326

Binary Harris Hawks Optimizer for High-Dimensional, Low Sample Size Feature Selectionchniques. The negative influence is due to the possibility of having many irrelevant and/or redundant features. In this chapter, a binary variant of recent Harris hawks optimizer (HHO) is proposed to boost the efficacy of wrapper-based feature selection techniques. HHO is a new fast and efficient sw

消音器

A Review of Grey Wolf Optimizer-Based Feature Selection Methods for Classifications. The area of feature selection deals reducing the dimensionality of data and selecting only the most relevant features to increase the classification performance and reduce the computational cost. This problem has exponential growth, which makes it challenging specially for datasets with a large n

种类

圆柱

Evolutionary Machine Learning Techniques978-981-32-9990-0Series ISSN 2524-7565 Series E-ISSN 2524-7573

unstable-angina

农学

流行

Aggressive

最低点

https://doi.org/10.1057/9780230348448sk. On the other hand, training of gradient descent algorithms suffers from being trapped in local optima and slow convergence speed in the last iterations. The moth-flame optimization (MFO) is a novel evolutionary method based on navigation paths of moths in nature. This algorithm showed its effect