Feature selection based on improved maximal relevance and minimal redundancy

Hao, Huijuan; Wang, Maoli; Tang, Yongwei

doi:10.1109/imcec.2016.7867449

Cited by 1 publication

(1 citation statement)

References 6 publications

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“…the nearest features to the target class. The MI is a relevance feature selection method that measures the information of two random variables in ML [30][31][32]. In other words, the MI refers to the extent of association between two random variables, i.e.…”

Section: Extracting and Simplifying The Lsc Of The Atomsmentioning

confidence: 99%

AtomicNet: a novel approach to identify the crystal structure of each simulated atom

Han¹,

Leilei²,

Wang³

et al. 2020

Modelling Simul. Mater. Sci. Eng.

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The study of crystal structure in the process of plastic deformation is essential to analyse the properties of materials and help to understand the deformation mechanism. With the rapid development of computer hardware and algorithms, great efforts have been paid for atomic simulation with computer science. However, most existing methods can only identify known structures in advance and consider unknown structure as unrecognized ones. In this work, we propose a method to identify crystal structure among atomistic simulations of crystalline materials. First, we develop a new characterization to describe each atom’s local space information, i.e. local spatial characteristics and utilize mutual information to simplify the characteristics. Then, the multi-layer perceptron neural network is used for the classification on the simplified characteristics. With the proposed method, we can not only identify the crystal structures of the surface of atoms, but also obtain the value of probability of each crystal structure. Our work provides significant insights for finding new mechanisms in structure transformation in material science field.

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Section: Extracting and Simplifying The Lsc Of The Atomsmentioning

confidence: 99%