A priori synthetic over-sampling methods for increasing classification sensitivity in imbalanced data sets

Rivera, William; Xanthopoulos, Petros

doi:10.1016/j.eswa.2016.09.010

Cited by 60 publications

(14 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…2. Oversampling The oversampling (upsampling) [17] method works by replicating observations from the minority class to obtain balanced data. The prominent disadvantage of oversampling is the likelihood that overfitting may be increased due to the extra copies of the minority class examples that are created.…”

Section: • Informed Undersamplingmentioning

confidence: 99%

A Hybrid Feature Selection Method RFSTL for Manufacturing Quality Prediction Based on a High Dimensional Imbalanced Dataset

Zhou

Chen

et al. 2021

IEEE Access

View full text Add to dashboard Cite

Under Industry 4.0, manufacturing quality prediction has been gaining increased interest from researchers and manufacturers. From the analysis of previous studies on quality predictions using machine learning, it became clear that the high dimensionality and imbalance of data are major and common problems affecting the learning performance. This work uses a hybrid method to address this issue, applying a Synthetic Minority Oversampling Technique & TomekLinks balancing approach to create balanced data and using Random Forest as the feature selecting measurement to reduce the dimensionality of data. In addition, a Fine Gaussian Support Vector Machine (Fine Gaussian SVM) based on the representative set of features selected by the hybrid method utilized is employed in this work to predict product quality. The results of experimentation demonstrate that the hybrid method proposed in this work performs well for manufacturing quality prediction and offers a simple, quick and powerful way to address the problem of feature selection encountered by the imbalanced classification.

show abstract

Section: • Informed Undersamplingmentioning

confidence: 99%

A Hybrid Feature Selection Method RFSTL for Manufacturing Quality Prediction Based on a High Dimensional Imbalanced Dataset

Zhou

Chen

et al. 2021

IEEE Access

View full text Add to dashboard Cite

show abstract

“…According to the dephosphorization thermodynamics and practical operation in Consteel electric furnace, the end-point phosphorus content is mainly determined by 17 process variables, including the chemical composition of hot metal, hot metal weight, scrap weight, lime weight, dolomite weight, carbon powder weight, smelting cycle, limestone weight, oxygen consumption, natural gas consumption, electricity consumption, end-point C content and end-point temperature. Because the industrial data con-tains numerous noise data which would disturb the model construction and result in incorrect results, 14,15) box-plot method is employed in this paper to filter the exceptional data out, and for the preprocessed data, the main statistics information of the process parameters is shown in Table 1. The symbols from X 1 to X 17 present the input process variables, and Y is the output variable.…”

Section: Collection Of Main Data Parametersmentioning

confidence: 99%

Prediction Model of End-point Phosphorus Content in Consteel Electric Furnace Based on PCA-Extra Tree Model

2021

View full text Add to dashboard Cite

According to the actual industrial data from a Consteel electric furnace, a prediction model based on the principal component analysis (PCA) and extremely randomized trees (Extra Tree model) is proposed for end-point phosphorus content. PCA is used to reduce the dimensionality of the input variable affecting the end-point phosphorus content and eliminate the collinearity among the input variables, and then the data transformed by PCA are used as input data for the established Extra tree model. Compared with other feature pre-processing methods, PCA method can greatly improve the regression prediction performance of the Extra Tree model. Finally, the validation by test set indicates that for the PCA-Extra Tree model, the hit rates of end-point phosphorus content are 98%, 96% and 89% with the prediction error range of ± 0.005%, ± 0.004% and ± 0.003%, respectively. The combined PCA-Extra Tree model has achieved the effective prediction for end-point phosphorus content, and provided a good reference for the end-point control and judgment of Consteel electric furnace.

show abstract

“…The original industrial data usually concentrate in a small area because of the predefined process schedule. Modeling with these data results in the prediction accurate in high density data area while inaccurate in low density data area . Therefore, the data should be balanced before modeling.…”

Section: Industrial Data Processingmentioning

confidence: 99%

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

Zhou

Cao

et al. 2018

steel research int.

View full text Add to dashboard Cite

To solve the contradiction between consumer's personal demand and mass production of steel enterprise, the optimal design for hot strip rolling of steel attracts researcher's attention. The core of the ensemble system is the artificial neural network (ANN) model, which is based on the industrial data. However, industrial data are difficult to be used for modeling because of their high dimension, low quality, and unbalanced. In current work, the method for industrial data processing is proposed for microalloyed steel. Based on the treated data, the mechanical property prediction models are established with the relative error of ±8% for yield strength (YS), ±6% for tensile strength (TS), and the absolute error of ±4% for elongation (EL), respectively. Combined with the multi‐objective optimization algorithm, the ANN models are implemented for designing the hot rolling process for S360 grade steel to control the stability of product quality.

show abstract

A priori synthetic over-sampling methods for increasing classification sensitivity in imbalanced data sets

Cited by 60 publications

References 27 publications

A Hybrid Feature Selection Method RFSTL for Manufacturing Quality Prediction Based on a High Dimensional Imbalanced Dataset

A Hybrid Feature Selection Method RFSTL for Manufacturing Quality Prediction Based on a High Dimensional Imbalanced Dataset

Prediction Model of End-point Phosphorus Content in Consteel Electric Furnace Based on PCA-Extra Tree Model

High‐Dimensional Data‐Driven Optimal Design for Hot Strip Rolling of Microalloyed Steel

Contact Info

Product

Resources

About