Ensemble Learning

Fernández, Alberto; García, Salvador; Galar, Mikel; Prati, Ronaldo C.; Krawczyk, Bartosz; Herrera, Francisco

doi:10.1007/978-3-319-98074-4_7

Cited by 6 publications

(3 citation statements)

References 102 publications

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“…In order to alleviate this problem, we remove part of the negative samples, which belong to the majority class, to make the data set relatively balanced. This technique is also known as undersampling . After undersampling, the ratio of the positive to negative sample is about 1 to 5.…”

Section: Methodsmentioning

confidence: 99%

Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery

Choo

Wee

Shen

et al. 2023

J. Chem. Inf. Model.

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Artificial Intelligence (AI) techniques are of great potential to fundamentally change antibiotic discovery industries. Efficient and effective molecular featurization is key to all highly accurate learning models for antibiotic discovery. In this paper, we propose a fingerprint-enhanced graph attention network (FinGAT) model by the combination of sequence-based 2D fingerprints and structure-based graph representation. In our feature learning process, sequence information is transformed into a fingerprint vector, and structural information is encoded through a GAT module into another vector. These two vectors are concatenated and input into a multilayer perceptron (MLP) for antibiotic activity classification. Our model is extensively tested and compared with existing models. It has been found that our FinGAT can outperform various state-of-the-art GNN models in antibiotic discovery.

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Section: Methodsmentioning

confidence: 99%

Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery

Choo

Wee

Shen

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

show abstract

“…The original database is naturally heterogeneous and imbalanced, as some compounds and frequency regions have been studied more intensely than others in the literature. This represents a challenge for the implementation of machine learning classification algorithms . Data sets with unequal data records per target feature lead to majority and minority classes, thus affecting the overall predictive accuracy of classification models …”

Section: Methodsmentioning

confidence: 99%

Machine Learning Identification of Organic Compounds Using Visible Light

et al. 2023

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Identifying chemical compounds is essential in several areas of science and engineering. Laser-based techniques are promising for autonomous compound detection because the optical response of materials encodes enough electronic and vibrational information for remote chemical identification. This has been exploited using the fingerprint region of infrared absorption spectra, which involves a dense set of absorption peaks that are unique to individual molecules, thus facilitating chemical identification. However, optical identification using visible light has not been realized. Using decades of experimental refractive index data in the scientific literature of pure organic compounds and polymers over a broad range of frequencies from the ultraviolet to the far-infrared, we develop a machine learning classifier that can accurately identify organic species based on a single-wavelength dispersive measurement in the visible spectral region, away from absorption resonances. The optical classifier proposed here could be applied to autonomous material identification protocols and applications.

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“…Due to the limitations of stand-alone models (Hapuarachchi and Wang, 2008;Hapuarachchi et al, 2011) and the advantages of ensemble models (Fernández et al, 2018;Zounemat-Kermani et al, 2020), in recent years, the use of ensemble models has expanded among researchers (Fernández et al, 2018;Zounemat-Kermani et al, 2020;Costache et al, 2021). Two ensemble models are used to derive the Flood Susceptibility Prediction Index and corresponding zonation maps.…”

Section: Ensemble Models Applied For Flood Susceptibility Prediction Index Computationmentioning

confidence: 99%

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

et al. 2021

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This study has developed a new ensemble model and tested another ensemble model for flood susceptibility mapping in the Middle Ganga Plain (MGP). The results of these two models have been quantitatively compared for performance analysis in zoning flood susceptible areas of low altitudinal range, humid subtropical fluvial floodplain environment of the Middle Ganga Plain (MGP). This part of the MGP, which is in the central Ganga River Basin (GRB), is experiencing worse floods in the changing climatic scenario causing an increased level of loss of life and property. The MGP experiencing monsoonal subtropical humid climate, active tectonics induced ground subsidence, increasing population, and shifting landuse/landcover trends and pattern, is the best natural laboratory to test all the susceptibility prediction genre of models to achieve the choice of best performing model with the constant number of input parameters for this type of topoclimatic environmental setting. This will help in achieving the goal of model universality, i.e., finding out the best performing susceptibility prediction model for this type of topoclimatic setting with the similar number and type of input variables. Based on the highly accurate flood inventory and using 12 flood predictors (FPs) (selected using field experience of the study area and literature survey), two machine learning (ML) ensemble models developed by bagging frequency ratio (FR) and evidential belief function (EBF) with classification and regression tree (CART), CART-FR and CART-EBF, were applied for flood susceptibility zonation mapping. Flood and non-flood points randomly generated using flood inventory have been apportioned in 70:30 ratio for training and validation of the ensembles. Based on the evaluation performance using threshold-independent evaluation statistic, area under receiver operating characteristic (AUROC) curve, 14 threshold-dependent evaluation metrices, and seed cell area index (SCAI) meant for assessing different aspects of ensembles, the study suggests that CART-EBF (AUCSR = 0.843; AUCPR = 0.819) was a better performant than CART-FR (AUCSR = 0.828; AUCPR = 0.802). The variability in performances of these novel-advanced ensembles and their comparison with results of other published models espouse the need of testing these as well as other genres of susceptibility models in other topoclimatic environments also. Results of this study are important for natural hazard managers and can be used to compute the damages through risk analysis.

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Ensemble Learning

Cited by 6 publications

References 102 publications

Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery

Fingerprint-Enhanced Graph Attention Network (FinGAT) Model for Antibiotic Discovery

Machine Learning Identification of Organic Compounds Using Visible Light

Flood Susceptibility Modeling in a Subtropical Humid Low-Relief Alluvial Plain Environment: Application of Novel Ensemble Machine Learning Approach

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