Applications of Hybridized Algorithms and Novel Algorithms in the Field of Machine Learning
P. Jeyanthi, A. Mansurali · 2021
Algorithms are data specific. In machine learning, there is no IDEAL algorithm available. One algorithm can not suit the best for all the problems. In the current era, the data are very huge; The effort of connecting information as a competitive catalyst is driving firms to higher levels of cognizance about how data is managed at its most basic level. Due to the usage of large metrics of data in day to day of e-era, the necessity of Hybridized algorithms and novel algorithms to handle the consistent increasing the data. Hybrid algorithms have been developed by combining two or more algorithms to improve or enhance overall efficiency to reach the optimization. Every scientists often try to use the benefits of every algorithms for the common good; at least that is the purpose in opinion. In practice, whether a hybrid can really achieve better performance is another matter, and finding ways to combine different algorithms to develop new algorithms is still an open problem. There are more than 40 other algorithms in the literature as reviewed in recent surveys and more algorithms are appearing.