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- research-articleFebruary 2024
Twin support vector quantile regression▪
Expert Systems with Applications: An International Journal (EXWA), Volume 237, Issue PAMar 2024https://doi.org/10.1016/j.eswa.2023.121239AbstractWe propose a twin support vector quantile regression (TSVQR) to capture the heterogeneous and asymmetric information in modern data. Using a quantile parameter, TSVQR effectively depicts the heterogeneous distribution information with respect to ...
Highlights- TSVQR is proposed to capture heterogeneous and asymmetric information.
- Two nonparallel planes measure asymmetric distribution at each quantile level.
- Two smaller-sized QPPs are solved with lower computational complexity.
- ...
- research-articleFebruary 2024
Robust and sparse canonical correlation analysis for fault detection and diagnosis using training data with outliers
Expert Systems with Applications: An International Journal (EXWA), Volume 236, Issue CFeb 2024https://doi.org/10.1016/j.eswa.2023.121434AbstractA well-known shortcoming of the traditional canonical correlation analysis (CCA) is the lack of robustness against outliers. This shortcoming hinders the application of CCA in the case where the training data contain outliers. To overcome this ...
- research-articleFebruary 2024
Developing a used car pricing model applying Multivariate Adaptive regression Splines approach
Expert Systems with Applications: An International Journal (EXWA), Volume 236, Issue CFeb 2024https://doi.org/10.1016/j.eswa.2023.121277AbstractAlthough the used car market in India is enormous, with an annual 27.1 billion USD worth of car sales, no academic study examining the pricing of Indian used cars is available. The average on-road life of cars in India is high, exceeding twenty ...
- research-articleMay 2023
Random feature selection using random subspace logistic regression
Expert Systems with Applications: An International Journal (EXWA), Volume 217, Issue CMay 2023https://doi.org/10.1016/j.eswa.2023.119535AbstractFeature selection becomes a prominent method in the big data era. The logistic regression model is a wrapper method that provides better classification or prediction accuracy but it is computationally expensive. In this study, we ...
Highlights- Random subspace regression helps improve the computational burden.
- Random ...
- research-articleMarch 2023
The dependence index based on martingale difference correlation: An efficient tool to distinguish different complex systems
Expert Systems with Applications: An International Journal (EXWA), Volume 213, Issue PCMar 2023https://doi.org/10.1016/j.eswa.2022.119284Highlights- We use the MDDM to construct the dependence index (DI) from the perspective of MDC.
For the study of complex systems, the existing correlation measures suffer from deficiencies and may lead to the loss of information and inefficient characterizations of complex systems. We aim to capture detailed dynamical ...
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- research-articleMarch 2023
Performance evaluation of automotive dealerships using grouped mixture of regressions
Expert Systems with Applications: An International Journal (EXWA), Volume 213, Issue PCMar 2023https://doi.org/10.1016/j.eswa.2022.119266AbstractFinite Mixture of Regressions (FMR) are among the most widely used models for dealing with heterogeneity in regression problems. FMR is a model-based clustering approach that models the data by assigning individual observations to one of the K ...
Highlights- Performance evaluation of automotive dealerships and retail stores in general.
- Clustering automotive dealerships and retail stores in general.
- Finite mixture of regressions with group structure.
- Posterior predictive density for ...
- research-articleFebruary 2023
WABL method as a universal defuzzifier in the fuzzy gradient boosting regression model
Expert Systems with Applications: An International Journal (EXWA), Volume 212, Issue CFeb 2023https://doi.org/10.1016/j.eswa.2022.118771Highlights- A different representation of triangular fuzzy numbers (TFN) is handled.
- ...
Gradient Boosting Regression (GBR) models are widely used and can give effective results in regression and classification problems. The main value of the approach proposed in this study is that it allows the GBR algorithm to be used ...
- research-articleDecember 2022
Forecasting Chinese provincial carbon emissions using a novel grey prediction model considering spatial correlation
Expert Systems with Applications: An International Journal (EXWA), Volume 209, Issue CDec 2022https://doi.org/10.1016/j.eswa.2022.118261Highlights- A two-stage background value calculation method is given.
- The novel grey model ...
In response to the errors caused by the uniform background value coefficients in the traditional grey model and the lack of analysis ability of panel data, this study proposes a two-stage background value calculation method and ...
- research-articleNovember 2022
Multivariate regression (MVR) and different artificial neural network (ANN) models developed for optical transparency of conductive polymer nanocomposite films
Expert Systems with Applications: An International Journal (EXWA), Volume 207, Issue CNov 2022https://doi.org/10.1016/j.eswa.2022.117937Highlights- MVR and different ANNs were used to model optical transparency for the first time.
The present study addresses a comparative performance assessment of multivariate regression (MVR) and well-optimized feed-forward, generalized regression and radial basis function neural network models which aimed to predict ...
- research-articleNovember 2022
A comparison of local explanation methods for high-dimensional industrial data: A simulation study
Expert Systems with Applications: An International Journal (EXWA), Volume 207, Issue CNov 2022https://doi.org/10.1016/j.eswa.2022.117918AbstractPrediction methods can be augmented by local explanation methods (LEMs) to perform root cause analysis for individual observations. But while most recent research on LEMs focus on low-dimensional problems, real-world datasets commonly ...
Highlights- Simulated high-dimensional process-like datasets with binary quality variable.
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- research-articleNovember 2022
Spectral knowledge-based regression for laser-induced breakdown spectroscopy quantitative analysis
Expert Systems with Applications: An International Journal (EXWA), Volume 205, Issue CNov 2022https://doi.org/10.1016/j.eswa.2022.117756Highlights- Spectral knowledge-based regression for LIBS data analysis.
- Nonlinear modelling with linear and physical interpretability.
- State of the art in LIBS quantitative analysis.
Laser-induced breakdown spectroscopy (LIBS) is a promising atomic emission spectroscopic technique for multi-elemental analysis and has the advantages of real-time multi-element measurement, minimal sample preparation and remote detection. ...
- research-articleOctober 2022
The tree based linear regression model for hierarchical categorical variables
Expert Systems with Applications: An International Journal (EXWA), Volume 203, Issue COct 2022https://doi.org/10.1016/j.eswa.2022.117423AbstractMany real-life applications consider nominal categorical predictor variables that have a hierarchical structure, e.g. economic activity data in Official Statistics. In this paper, we focus on linear regression models built in the ...
Highlights- We study linear regression models built on hierarchical categorical predictors.
- research-articleSeptember 2022
Regression random machines: An ensemble support vector regression model with free kernel choice
Expert Systems with Applications: An International Journal (EXWA), Volume 202, Issue CSep 2022https://doi.org/10.1016/j.eswa.2022.117107AbstractMachine learning techniques have one of their main objectives to reduce the generalized prediction error. Support vector models have as a main challenge the choice of an appropriate kernel function, as well as the estimation of its ...
Highlights- Random Machines presents a new ensemble method using support vector models.
- Eliminates the choice of kernel function and simplify the tuning process.
- Discussion about the strength and diversity trade-off in ensemble learning ...
- research-articleAugust 2022
Parameter-free surrounding neighborhood based regression methods
Expert Systems with Applications: An International Journal (EXWA), Volume 199, Issue CAug 2022https://doi.org/10.1016/j.eswa.2022.116881AbstractIn machine learning, nearest neighbor (NN) regression is one of the most prominent methods for numeric prediction. It estimates the output variable of a new data point by averaging the output variables of the neighboring points. The ...
Highlights- Parameter-free surrounding neighborhood (PSN) is introduced for NN regression.
- ...
- research-articleApril 2022
Empirical investigation of hyperparameter optimization for software defect count prediction
Expert Systems with Applications: An International Journal (EXWA), Volume 191, Issue CApr 2022https://doi.org/10.1016/j.eswa.2021.116217Highlights- Examine effect of hyperparameters on regression techniques for software defect count prediction.
Prior identification of defects in software modules can help testers to allocate limited resources efficiently. Defect prediction techniques are helpful for this situation because they allow testers to identify and focus on defect ...
- research-articleDecember 2021
Bias and variance residuals for machine learning nonlinear simplex regressions
Expert Systems with Applications: An International Journal (EXWA), Volume 185, Issue CDec 2021https://doi.org/10.1016/j.eswa.2021.115656AbstractWe propose two new residuals that can be used to evaluate the bias and variance of nonlinear simplex regressions for machine learning. Such models are supervised learning tools for problems with high complexity, since they involve ...
Highlights- Nonlinear simplex regression models can be used for supervised machine learning.
- research-articleDecember 2021
Weighted Clusterwise Linear Regression based on adaptive quadratic form distance
Expert Systems with Applications: An International Journal (EXWA), Volume 185, Issue CDec 2021https://doi.org/10.1016/j.eswa.2021.115609AbstractThe standard approach to Clusterwise Regression is the Clusterwise Linear Regression method. This approach can lead to data over-fitting, and it is not able to distinguish linear relationships in groups of observations well separated ...
Highlights- A Weighted Clusterwise Regression to obtain homogeneous clusters.
- Objective ...
- research-articleNovember 2021
Improving stock market volatility forecasts with complete subset linear and quantile HAR models
Expert Systems with Applications: An International Journal (EXWA), Volume 183, Issue CNov 2021https://doi.org/10.1016/j.eswa.2021.115416AbstractVolatility forecasting plays an integral role in risk management, investments and security valuation for all assets with uncertain future payoffs. We enrich the literature by presenting computationally intensive variations of the ...
Highlights- We design complete subset linear (CSLR) and quantile regression (CSQR) HAR models.
- research-articleSeptember 2021
Robust machine-learning workflow for subsurface geomechanical characterization and comparison against popular empirical correlations
Expert Systems with Applications: An International Journal (EXWA), Volume 177, Issue CSep 2021https://doi.org/10.1016/j.eswa.2021.114942Highlights- Recovery of fossil/ geothermal energy requires subsurface mechanical characterization.
Accurate subsurface geomechanical characterization is critical for fossil and geothermal energy recovery and extraction of earth resources. Compressional and shear travel time logs (DTC and DTS) acquired using sonic logging tools ...
- research-articleAugust 2021
A fuzzy penalized regression model with variable selection
Expert Systems with Applications: An International Journal (EXWA), Volume 175, Issue CAug 2021https://doi.org/10.1016/j.eswa.2021.114696Highlights- Fuzzy penalized regression model.
- Fuzzy output variable and crisp input ...
In the classical multiple regression modeling, there might be some insignificant input variables. These variables can be eliminated by automatic selectors, known as penalized methods. We propose a penalized estimation method for the ...