A Review on Dimensionality Reduction for Machine Learning

Coelho, Duarte; Madureira, Ana; Pereira, Ivo

doi:10.1007/978-3-031-27499-2_27

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Feature selection is usually necessary in machine learning, as it identifies the optimal relevant features and removes irrelevant ones. , The imbalance between the dimensions of the feature space and the data points in the space can negatively affect the performance of machine learning models . Ineffective descriptors with constant, duplicate, and/or missing values are first removed.…”

Section: Methodsmentioning

confidence: 99%

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals

Song

Yang

2023

J. Chem. Inf. Model.

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Accurate determination of the thermal rate constants for combustion reactions is a highly challenging task, both experimentally and theoretically. Machine learning has been proven to be a powerful tool to predict reaction rate constants in recent years. In this work, three supervised machine learning algorithms, including XGB, FNN, and XGB-FNN, are used to develop quantitative structure−property relationship models for the estimation of the rate constants of hydrogen abstraction reactions from alkanes by the free radicals CH 3 , H, and O. The molecular similarity based on Morgan molecular fingerprints combined with the topological indices are proposed to represent chemical reactions in the machine learning models. Using the newly constructed descriptors, the hybrid XGB-FNN algorithm yields average deviations of 65.4%, 12.1%, and 64.5% on the prediction sets of alkanes + CH 3 , H, and O, respectively, whose performance is comparable and even superior to the corresponding one using the activation energy as a descriptor. The use of activation energy as a descriptor has previously been shown to significantly improve prediction accuracy (Fuel 2022, 322, 124150) but typically requires cumbersome ab initio calculations. In addition, the XGB-FNN models could reasonably predict reaction rate constants of hydrogen abstractions from different sites of alkanes and their isomers, indicating a good generalization ability. It is expected that the reaction descriptors proposed in this work can be applied to build machine learning models for other reactions.

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Section: Methodsmentioning

confidence: 99%

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals

Song

Yang

2023

J. Chem. Inf. Model.

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Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

Zhang,

Xia,

Song

et al. 2024

J. Phys. Chem. A

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Calculating the thermal rate constants of elementary combustion reactions is of great importance in theoretical chemistry. Machine learning has become a powerful, data-driven method for predicting rate constants nowadays. Recently, the molecular similarity combined with the topological indices were proposed to represent the hydrogen abstraction reactions of alkane [J. Chem. Inf. Model. 2023, 63, 5097−5106], which are, however, not applicable to alkane cracking reactions, another important class of combustion reactions, due to the cleavage of the C−C bond. In this work, a new feature selection scheme is proposed to describe both bimolecular and unimolecular cracking reactions. Molecular descriptors are elaborately selected individually for both reactants and products from those generated by the open-source software RDKit. Machine learning models combined with these molecular descriptors are proven to have the ability to accurately predict rate constants of both the hydrogen abstraction reactions of alkanes by CH 3 and the alkane cracking reactions. The average deviation of the XGB-FNN model for prediction is around 60% for the hydrogen abstraction reactions of alkanes and 100% for the alkane cracking reactions. It is expected that the descriptors proposed in this work can be applied to build machine learning models for other reactions.

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A Review on Dimensionality Reduction for Machine Learning

Cited by 2 publications

References 36 publications

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

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A Review on Dimensionality Reduction for Machine Learning

Cited by 2 publications

References 36 publications

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH3/H/O Radicals

Predicting Rate Constants of Alkane Cracking Reactions Using Machine Learning

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Product

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Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals

Structure-Based Reaction Descriptors for Predicting Rate Constants by Machine Learning: Application to Hydrogen Abstraction from Alkanes by CH₃/H/O Radicals