Decision Tree Twin Support Vector Machine Based on Kernel Clustering for Multi-class Classification

Dou, Qingyun; Zhang, Li

doi:10.1007/978-3-030-04212-7_25

Cited by 4 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Once two children nodes are created with corresponding labels, they are passed through the TWSVM [22,23]. For generating the TWSVM, the data is split into 80% training and 20% testing data.…”

Section: Theoretical Modelmentioning

confidence: 99%

“…Following the work of Dou and Zhang (2018), a decision tree twin support vector machine based on kernel clustering (DT 2 -SVM-KC) method is proposed in this paper for multiclass-classification [22]. This structure or approach of classification considers twin support vectors as opposed to a single support vector in traditional SVM.…”

Section: Introductionmentioning

confidence: 99%

“…On the other hand, decision trees based on recursive binary splitting are used extensively for classification purposes [25]. As a combination of TWSVM and decision tree, the proposed approach is appropriate to deal with multiclass classification problems with better generalization performance with lesser computation time [22].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Phull

Egas

Barui

et al. 2019

Metals

View full text Add to dashboard Cite

Ensuring the high quality of end product steel by removing phosphorus content in Basic Oxygen Furnace (BOF) is essential and otherwise leads to cold shortness. This article aims at understanding the dephosphorization process through end-point P-content in BOF steelmaking based on data-mining techniques. Dephosphorization is often quantified through the partition ratio ( l p ) which is the ratio of wt% P in slag to wt% P in steel. Instead of predicting the values of l p , the present study focuses on the classification of final steel based on slag chemistry and tapping temperature. This classification signifies different degrees (‘High’, ‘Moderate’, ‘Low’, and ‘Very Low’) to which phosphorus is removed in the BOF. Data of slag chemistry and tapping temperature collected from approximately 16,000 heats from two steel plants (Plant I and II) were assigned to four categories based on unsupervised K-means clustering method. An efficient decision tree-based twin support vector machines (TWSVM) algorithm was implemented for category classification. Decision trees were constructed using the concepts: Gaussian mixture model (GMM), mean shift (MS) and affinity propagation (AP) algorithm. The accuracy of the predicted classification was assessed using the classification rate (CR). Model validation was carried out with a five-fold cross validation technique. The fitted model was compared in terms of CR with a decision tree-based support vector machines (SVM) algorithm applied to the same data. The highest accuracy (≥97%) was observed for the GMM-TWSVM model, implying that by manipulating the slag components appropriately using the structure of the model, a greater degree of P-partition can be achieved in BOF.

show abstract

Section: Theoretical Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Phull

Egas

Barui

et al. 2019

Metals

View full text Add to dashboard Cite

show abstract

“…Further, Gao et al introduced an ensemble algorithm combining k-nearest neighbor-based weighted twin support vector regression and Lévy flight whale optimization algorithm [13]. Duo and Zhang implemented a novel multiclass classifier decision tree twin support vector machine based on kernel clustering algorithm [14]. More recently, Phull et al studied a decision tree-based twin support vector machine to assess the performance of a dephosphorization process using end-point phosphorus content in two BOF steelmaking plants [15].…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, these works imply significant and successful applications of machine learning models for the end-point predictions in BOF. However, either most of these models project and explore dephosphorization as a regression problem [10,11], or not many machine learning models are applied specifically to the dephosphorization process, but rather used to study other measurable components of a BOF process [13,14]. As a result, a detailed investigation is required to further explore the potential of machine learning classifiers (rather than regressors), e.g., support vector machines (SVMs) or other variants of SVM to study the non-linear complex relationships among variables in a BOF process, specifically for dephosphorization.…”

Section: Introductionmentioning

confidence: 99%

Least Squares Twin Support Vector Machines to Classify End-Point Phosphorus Content in BOF Steelmaking

Barui

Mukherjee

et al. 2022

Metals

View full text Add to dashboard Cite

End-point phosphorus content in steel in a basic oxygen furnace (BOF) acts as an indicator of the quality of manufactured steel. An undesirable amount of phosphorus is removed from the steel by the process of dephosphorization. The degree of phosphorus removal is captured numerically by the ‘partition ratio’, given by the ratio of %wt phosphorus in slag and %wt phosphorus in steel. Due to the presence of multitudes of process variables, often, it is challenging to predict the partition ratio based on operating conditions. Herein, a robust data-driven classification technique of least squares twin support vector machines (LSTSVM) is applied to classify the ‘partition ratio’ to two categories (‘High’ and ‘Low’) steels indicating a greater or lesser degree of phosphorus removal, respectively. LSTSVM is a simpler, more robust, and faster alternative to the twin support vector machines (TWSVM) with respect to non-parallel hyperplanes-based binary classifications. The relationship between the ‘partition ratio’ and the chemical composition of slag and tapping temperatures is studied based on approximately 16,000 heats from two BOF plants. In our case, a relatively higher model accuracy is achieved, and LSTSVM performed 1.5–167 times faster than other applied algorithms.

show abstract

Feature Selection Using Sparse Twin Bounded Support Vector Machine

Zheng

Zhang

Yan

2020

Neural Information Processing

View full text Add to dashboard Cite

Decision Tree Twin Support Vector Machine Based on Kernel Clustering for Multi-class Classification

Cited by 4 publications

References 14 publications

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

An Application of Decision Tree-Based Twin Support Vector Machines to Classify Dephosphorization in BOF Steelmaking

Least Squares Twin Support Vector Machines to Classify End-Point Phosphorus Content in BOF Steelmaking

Feature Selection Using Sparse Twin Bounded Support Vector Machine

Contact Info

Product

Resources

About