As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded...As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.展开更多
A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set l...A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.展开更多
Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as f...Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods： multiple kernel learning-based rescaling （MKL-SVM） and kernel-target alignment-based rescaling （KTA-SVM）. MKL-SVM makes use of the framework of multiple kernel learning （MKL） and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed meth- ods were compared with three other methods： an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general.展开更多
文摘As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.
基金supported by the National Natural Science Key Foundation of China(69974021)
文摘A new incremental support vector machine （SVM） algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to improve the performance of SVM. Simulation results indicate that the proposed algorithm can not only solve the model selection problem in SVM incremental learning, but also improve the classification or prediction precision.
基金Supported by the National Natural Science Foundation of China(Nos. 30625012 and 60721003)
文摘Support vector machines （SVMs） have widespread use in various classification problems. Although SVMs are often used as an off-the-shelf tool, there are still some important issues which require improvement such as feature rescaling. Standardization is the most commonly used feature rescaling method. However, standardization does not always improve classification accuracy. This paper describes two feature rescaling methods： multiple kernel learning-based rescaling （MKL-SVM） and kernel-target alignment-based rescaling （KTA-SVM）. MKL-SVM makes use of the framework of multiple kernel learning （MKL） and KTA-SVM is built upon the concept of kernel alignment, which measures the similarity between kernels. The proposed meth- ods were compared with three other methods： an SVM method without rescaling, an SVM method with standardization, and SCADSVM. Test results demonstrate that different rescaling methods apply to different situations and that the proposed methods outperform the others in general.
