A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Gu, Xinping; Han, Yemei; Yu, Junfu

doi:10.1109/access.2020.2964294

Cited by 55 publications

(31 citation statements)

References 49 publications

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“…XGBoost algorithm is improved based on GBDT (Gradient Boosting Decision Tree) algorithm [26], which is a kind of supervision algorithm [27]. The idea is to establish a certain number of classification regression trees, so that the predicted number value of the tree group is as close to the real number value as possible and has the greatest generalization ability [28].…”

Section: (4) Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

2020

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Due to the lack of data available for training, deep learning hardly performed well in the field of garbage image classification. We choose the TrashNet data set which is widely used in the field of garbage image classification, and try to overcome data deficiencies in this field by optimizing the network structure. In this paper, it is found that the deeper network and short-circuit connection, which are generally accepted in the field of deep learning, will not work well on the TrashNet data set. By analyzing and modifying the network structure, we propose an effective method to improve the network performance on TrashNet data set. This method widens the network by expanding branches, and then uses add layers to realize the fusion of feature information. It can make full use of feature information at slight additional computational cost. Using this method to replace the core structure of the Xception network, the performance of the improved network has been improved greatly. Finally, the M-b Xception network proposed by us achieves 94.34% classification accuracy on the TrashNet data set, and has certain advantages over some state-of-theart methods on multiple indicators. The python code can be download from https://github.com/scp19801980/Trash-classify-M_b-Xception.

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Section: (4) Extreme Gradient Boosting (Xgboost)mentioning

confidence: 99%

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

2020

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“…Because of its excellent performance and low computational complexity, it has been widely used in industry. 27 The hyperparameters have great impact on its accuracy, so it is realistic to study the HPO of XGBoost. In this research, the XGBoost method is implemented from XGBoost package and its hyperparameters to be optimized are described in Table 6.…”

Section: Case 2: Workpiece Quality Datasetmentioning

confidence: 99%

A new automatic machine learning based hyperparameter optimization for workpiece quality prediction

Wen

Gao

2020

Measurement and Control

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Workpiece quality prediction is very important in modern manufacturing industry. However, traditional machine learning methods are very sensitive to their hyperparameters, making the tuning of the machine learning methods essential to improve the prediction performance. Hyperparameter optimization (HPO) approaches are applied attempting to tune hyperparameters, such as grid search and random search. However, the hyperparameters space for workpiece quality prediction model is high dimension and it consists with continuous, combinational and conditional types of hyperparameters, which is difficult to be tuned. In this article, a new automatic machine learning based HPO, named adaptive Tree Pazen Estimator (ATPE), is proposed for workpiece quality prediction in high dimension. In the proposed method, it can iteratively search the best combination of hyperparameters in the automatic way. During the warm-up process for ATPE, it can adaptively adjust the hyperparameter interval to guide the search. The proposed ATPE is tested on sparse stack autoencoder based MNIST and XGBoost based WorkpieceQuality dataset, and the results show that ATPE provides the state-of-the-art performances in high-dimensional space and can search the hyperparameters in reasonable range by comparing with Tree Pazen Estimator, annealing, and random search, showing its potential in the field of workpiece quality prediction.

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“…In existing studies, the adopted algorithms seem to be relatively traditional; examples include multiple linear regression (MLR) [27,29], neural network [30,20], gradient boost decision tree (GBDT) [31]. In recent years, data scientists have proposed a variety of novel machine learning algorithms such as XGBoost [32] and LightGBM [33], which have been proven to have better performance than traditional methods in a number of application fields [34][35][36][37].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

et al. 2020

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Limited driving range is one of the major obstacles to the widespread application of electric vehicles (EVs). Accurately predicting the remaining driving range can effectively reduce the range anxiety of drivers. In this paper, a blended machine learning model was proposed to predict the remaining driving range of EVs based on real-world historical driving data. The blended model fuses two advanced machine learning algorithms of Extreme Gradient Boosting Regression Tree (XGBoost) and Light Gradient Boosting Regression Tree (LightGBM). The proposed model was trained to "learn" the relationship between the driving distance and the proposed features such as cumulative output energy of the motor and the battery, different driving patterns, and temperature of the battery). In addition, an "anchor (baseline) based" strategy was proposed and was seen to be able to effectively eliminate the unbalance distribution of dataset. The results of experiments suggest that our proposed anchor-based blended model has better performances with a smaller prediction error range of [-0.8, 0.8] as compared with previous methods. INDEX TERMS electric vehicle, remaining driving range estimation, machine learning, data mining.

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A Novel Lane-Changing Decision Model for Autonomous Vehicles Based on Deep Autoencoder Network and XGBoost

Cited by 55 publications

References 49 publications

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

A Novel Multi-Branch Channel Expansion Network for Garbage Image Classification

A new automatic machine learning based hyperparameter optimization for workpiece quality prediction

Machine Learning-Based Method for Remaining Range Prediction of Electric Vehicles

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