A Novel Genetic Algorithm Approach to Simultaneous Feature Selection and Instance Selection

Albuquerque, Inti Mateus Resende; Nguyen, Bach Hoai; Xue, Bing; Zhang, Mengjie

doi:10.1109/ssci47803.2020.9308307

Cited by 10 publications

(1 citation statement)

References 35 publications

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“…Other work [64] combined both feature and instance selection in a cooperative coevolutionary framework to improve classification performance while simultaneously reducing the overall size of the dataset. Similarly, Albuquerque et al [65] proposed representing each candidate solution as two binary vectors to determine selected features and instances. Although these approaches do lower the computational cost of each individual fitness evaluation (since the total number of instances is reduced), the overall search complexity explodes due to joint optimization in instance and feature spaces, leading to a potential convergence slowdown.…”

Section: Evolutionary Feature Selectionmentioning

confidence: 99%

Lean evolutionary machine learning by multitasking simpler and hard tasks

Zhang¹

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I would also like to thank my Thesis Advisory Committee (TAC) for their valuable insights and suggestions for my research. Many thanks to Ray Lim, Chen Zhe, and other researchers who I have met during my Ph.D journey. They have always kept my morale high and played a significant role in my personal development.Special thanks to Ms. Lim Siew Hoon and Mr. Suvindaren Subramaniam for all the opportunities given to me in my professional career.Finally, words cannot express my gratitude to my partner, May, my cat Bobby and my parents for their unwavering support and understanding throughout my Ph.D

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Section: Evolutionary Feature Selectionmentioning

confidence: 99%