A novel adaptive memetic binary optimization algorithm for feature selection

Feature selection (FS) determines the beneficial features in data and decreases the disadvantages of the curse of dimensionality. This work proposes a novel adaptive memetic binary optimization (AMBO) algoraaithm for FS. FS is an NP-Hard binary optimization problem. AMBO is a pure binary optimizatio...

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Vydané v:	The Artificial intelligence review Ročník 56; číslo 11; s. 13463 - 13520
Hlavný autor:	Cinar, Ahmet Cevahir
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Dordrecht Springer Netherlands 01.11.2023 Springer Springer Nature B.V
Predmet:	Algorithms Artificial Intelligence Averages Candidates Classification Computer Science Datasets Evolutionary algorithms Evolutionary computation Experiments Exploitation Feature selection Function words Genetic algorithms Genetics Heuristic methods Logic circuits Mathematical optimization Novels Optimization Optimization algorithms Particle swarm optimization Population growth Rank tests Statistical significance Strategies Tournaments Transfer functions Memetic computing Logic gates Local search Feature selection Binary optimization
ISSN:	0269-2821, 1573-7462
On-line prístup:	Získať plný text
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