Applying REC Analysis to Ensembles of Sigma-Point Kalman Filters

Pina, Aloísio Carlos de; Zaverucha, Gerson

doi:10.1007/11840930_16

Cited by 4 publications

(4 citation statements)

References 8 publications

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“…In classification tasks, the receiver operating characteristic (ROC) curve is widely used as a useful tool for comparing and displaying classification results. Regression error characteristic (REC) curves are developed in the domain of regression for similar purpose as ROC [102][103][104][105] . The proportion of correctly predicted occurrences within a certain tolerance interval (y-axis) are shown versus the absolute deviation tolerance (x-axis) in REC curves.…”

Section: Regression Error Characteristic Curvementioning

confidence: 99%

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Javed,

Shahab,

Asif

et al. 2024

Sci Rep

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The escalation of global urbanization and industrial expansion has resulted in an increase in the emission of harmful substances into the atmosphere. Evaluating the effectiveness of titanium dioxide (TiO2) in photocatalytic degradation through traditional methods is resource-intensive and complex due to the detailed photocatalyst structures and the wide range of contaminants. Therefore in this study, recent advancements in machine learning (ML) are used to offer data-driven approach using thirteen machine learning techniques namely XG Boost (XGB), decision tree (DT), lasso Regression (LR2), support vector regression (SVR), adaBoost (AB), voting Regressor (VR), CatBoost (CB), K-Nearest Neighbors (KNN), gradient boost (GB), random Forest (RF), artificial neural network (ANN), ridge regression (RR), linear regression (LR1) to address the problem of estimation of TiO2 photocatalytic degradation rate of air contaminants. The models are developed using literature data and different methodical tools are used to evaluate the developed ML models. XGB, DT and LR2 models have high R2 values of 0.93, 0.926 and 0.926 in training and 0.936, 0.924 and 0.924 in test phase. While ANN, RR and LR models have lowest R2 values of 0.70, 0.56 and 0.40 in training and 0.62, 0.63 and 0.31 in test phase respectively. XGB, DT and LR2 have low MAE and RMSE values of 0.450 min-1/cm2, 0.494 min-1/cm2 and 0.49 min-1/cm2 for RMSE and 0.263 min-1/cm2, 0.285 min-1/cm2 and 0.29 min-1/cm2 for MAE in test stage. XGB, DT, and LR2 have 93% percent errors within 20% error range in training phase. XGB has 92% and DT, and LR2 have 94% errors with 20% range in test phase. XGB, DT, LR2 models remained the highest performing models and XGB is the most robust and effective in predictions. Feature importances reveal the role of input parameters in prediction made by developed ML models. Dosage, humidity, UV light intensity remain important experimental factors. This study will impact positively in providing efficient models to estimate photocatalytic degradation rate of air contaminants using TiO2.

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Section: Regression Error Characteristic Curvementioning

confidence: 99%

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Javed,

Shahab,

Asif

et al. 2024

Sci Rep

View full text Add to dashboard Cite

show abstract

“…For regression problems, usually the mean squared error of the ensemble is used. However, recent works [26] point the analysis of regression error characteristic curves as a technique more adequate to the selection of estimators in an ensemble.…”

Section: Methods To Select Componentsmentioning

confidence: 99%

“…It is only required that sufficient statistics are propagated for each particle. In this research we use SRUKF and SRCDKF as the importance density generators, since they showed to provide better approximations in previous works [26].…”

Section: Particle Filtersmentioning

confidence: 99%

“…A model tree [43] predictor was chosen as a metaregressor, since it is a state-of-the-art regression method and it has already been successfully used as a metaclassifier for stacking [44], outperforming all the other combining methods tested. More recently, Model Trees showed to provide high performance as meta-regressors for stackings of time series predictors [26]. In order to determine which subset of algorithms can provide the best ensemble, we built stacking models by using a wrapper approach [45]: cross validation is used to estimate the AOC of the REC curve for the ensemble for a set of components.…”

Section: Ensemble Of Particles Filtersmentioning

confidence: 99%

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Applying REC analysis to ensembles of particle filters

Pina

Zaverucha

2008

Neural Comput & Applic

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Particle filters (PF) are sequential Monte Carlo methods based in the representation of probability densities with mass points. Although currently most researches involving time series forecasting use the traditional methods, particle filters can be applied to any state-space model and generalize the traditional Kalman filter methods, providing better results. Furthermore, it is well-known that for classification and regression tasks ensembles achieve better performances than the algorithms that compose them. Therefore, it is expected that ensembles of time series predictors can provide even better results than particle filters. The regression error characteristic (REC) analysis is a powerful technique for visualization and comparison of regression models. The objective of this work is to advocate the use of REC curves in order to compare traditional Kalman filter methods with particle filters and analyze their use in ensembles, which can achieve a better performance.

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Bootstrap Methods for Foreign Currency Exchange Rates Prediction

Shen

2007

2007 International Joint Conference on Neural Networks

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Applying REC Analysis to Ensembles of Sigma-Point Kalman Filters

Cited by 4 publications

References 8 publications

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Evaluation of machine learning models for predicting TiO2 photocatalytic degradation of air contaminants

Applying REC analysis to ensembles of particle filters

Bootstrap Methods for Foreign Currency Exchange Rates Prediction

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