Multi‐Layer Feature Selection Incorporating Weighted Score‐Based Expert Knowledge toward Modeling Materials with Targeted Properties
暂无分享,去创建一个
Yue Liu | Junming Wu | Siqi Shi | Maxim Avdeev | S. Shi | M. Avdeev | Junming Wu | Yue Liu
[1] Jinlan Wang,et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning , 2018, Nature Communications.
[2] Ferat Sahin,et al. A survey on feature selection methods , 2014, Comput. Electr. Eng..
[3] Atsuto Seko,et al. Representation of compounds for machine-learning prediction of physical properties , 2016, 1611.08645.
[4] W. Chunmei,et al. Prediction of lattice constant in perovskites of GdFeO3 structure , 2003 .
[5] Tomasz Arodz,et al. Computational methods in developing quantitative structure-activity relationships (QSAR): a review. , 2006, Combinatorial chemistry & high throughput screening.
[6] Leroy Cronin,et al. Controlling an organic synthesis robot with machine learning to search for new reactivity , 2018, Nature.
[7] Min Song,et al. An approach for the aging process optimization of Al–Zn–Mg–Cu series alloys , 2009 .
[8] M. Shahlaei. Descriptor selection methods in quantitative structure-activity relationship studies: a review study. , 2013, Chemical reviews.
[9] Fuhui Long,et al. Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy , 2003, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[10] Heather J Kulik,et al. Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure-Property Relationships. , 2017, The journal of physical chemistry. A.
[11] Yue Liu,et al. Predicting the onset temperature (Tg) of GexSe1-x glass transition: a feature selection based two-stage support vector regression method. , 2019, Science bulletin.
[12] Deron Liang,et al. Novel feature selection methods to financial distress prediction , 2014, Expert Syst. Appl..
[13] Fei Yuan,et al. Chemical Descriptors Are More Important Than Learning Algorithms for Modelling , 2012, Molecular informatics.
[14] Özge Uncu,et al. A novel feature selection approach: Combining feature wrappers and filters , 2007, Inf. Sci..
[15] Ruijie Zhang,et al. An materials informatics approach to Ni-based single crystal superalloys lattice misfit prediction , 2018 .
[16] K. Fujimura,et al. Accelerated Materials Design of Lithium Superionic Conductors Based on First‐Principles Calculations and Machine Learning Algorithms , 2013 .
[17] Wei Li,et al. Predicting the thermodynamic stability of perovskite oxides using machine learning models , 2018, Computational Materials Science.
[18] Pascal Mougin,et al. Prediction of Density and Viscosity of Biofuel Compounds Using Machine Learning Methods , 2012 .
[19] David A. Winkler,et al. Capturing the Crystal: Prediction of Enthalpy of Sublimation, Crystal Lattice Energy, and Melting Points of Organic Compounds , 2013, J. Chem. Inf. Model..
[20] Su Qiang,et al. Two semi-empirical approaches for the prediction of oxide ionic conductivities in ABO3 perovskites , 2009 .
[21] Lean Yu,et al. Feature weighted confidence to incorporate prior knowledge into support vector machines for classification , 2018, Knowledge and Information Systems.
[22] V. Rodriguez-Galiano,et al. Feature selection approaches for predictive modelling of groundwater nitrate pollution: An evaluation of filters, embedded and wrapper methods. , 2018, The Science of the total environment.
[23] M. Johan,et al. Optimization of neural network for ionic conductivity of nanocomposite solid polymer electrolyte system (PEO–LiPF6–EC–CNT) , 2012 .
[24] Jean-Michel Poggi,et al. Variable selection using random forests , 2010, Pattern Recognit. Lett..
[25] Turker Tekin Erguzel,et al. A wrapper-based approach for feature selection and classification of major depressive disorder-bipolar disorders , 2015, Comput. Biol. Medicine.
[26] Siqi Shi,et al. The onset temperature (Tg) of AsxSe1−x glasses transition prediction: A comparison of topological and regression analysis methods , 2017 .
[27] Wenliang Chen,et al. Prediction of mechanical properties of carbon fiber based on cross-scale FEM and machine learning , 2019, Composite Structures.
[28] Klaus-Robert Müller,et al. Finding Density Functionals with Machine Learning , 2011, Physical review letters.
[29] Peter C. Jurs,et al. Automated Descriptor Selection for Quantitative Structure-Activity Relationships Using Generalized Simulated Annealing , 1995, J. Chem. Inf. Comput. Sci..
[30] Gaël Varoquaux,et al. Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..
[31] Daniel C Elton,et al. Applying machine learning techniques to predict the properties of energetic materials , 2018, Scientific Reports.