Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

Qu, Yi; Lin, Zhengkui; Li, Honglei; Zhang, Xiaonan

doi:10.1109/access.2019.2952655

Cited by 65 publications

(24 citation statements)

References 28 publications

(25 reference statements)

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“…Furthermore, a previous study has reported positive and promising result by integrating GA as a feature selection method and XGBoost as a classification model. Qu et al (2019) proposed GA-XGBoost to identify the characteristics/features related with traffic accidents [32]. They applied the model into big data of traffic accidents in 7 cities in China.…”

Section: Extreme Gradient Boosting (Xgboost) and Genetic Algorithms (Ga)mentioning

confidence: 99%

“…Genetic algorithm (GA) is a feature selection method that has been used to find the best feature subsets [24,25] and shown significant advantage for improving the performance of emotional stress state detection [26], severe chronic disorders of consciousness prediction [27], children's activities regarding recognition and classification [28], gene encoder [29], hepatitis prediction [30], and coronavirus disease (COVID-19) patient detection [31]. A previous study revealed that a combination of GA and XGBoost has improved the model performance [32]. However, to the best of our knowledge, none of the previous studies have combined GA and XGBoost together to improve the performance of the model, especially for the case of self-care classification.…”

Section: Introductionmentioning

confidence: 99%

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A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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Detecting self-care problems is one of important and challenging issues for occupational therapists, since it requires a complex and time-consuming process. Machine learning algorithms have been recently applied to overcome this issue. In this study, we propose a self-care prediction model called GA-XGBoost, which combines genetic algorithms (GAs) with extreme gradient boosting (XGBoost) for predicting self-care problems of children with disability. Selecting the feature subset affects the model performance; thus, we utilize GA to optimize finding the optimum feature subsets toward improving the model’s performance. To validate the effectiveness of GA-XGBoost, we present six experiments: comparing GA-XGBoost with other machine learning models and previous study results, a statistical significant test, impact analysis of feature selection and comparison with other feature selection methods, and sensitivity analysis of GA parameters. During the experiments, we use accuracy, precision, recall, and f1-score to measure the performance of the prediction models. The results show that GA-XGBoost obtains better performance than other prediction models and the previous study results. In addition, we design and develop a web-based self-care prediction to help therapist diagnose the self-care problems of children with disabilities. Therefore, appropriate treatment/therapy could be performed for each child to improve their therapeutic outcome.

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Section: Extreme Gradient Boosting (Xgboost) and Genetic Algorithms (Ga)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

et al. 2020

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“…To avoid the sampling errors, the distribution of all the samples in the training and testing dataset are the same as the original set. Then, the parameters of base XGBoost, KNN and SVM classifier are well-tuned based on parameter adjustment guidelines in [12,21,31]. And the optimal hyperparameters of AL-XGBoost are selected by BO technique, and we set the early stopping rounds as 200 to reduce the optimization time.…”

Section: A Experimental Settingmentioning

confidence: 99%

“…During the training process of XGBoost classifier, the training set size is generally configured large enough to achieve a high classification accuracy [21]. However, the huge size of training set may lead to a time-consuming training process, especially for a CPES with multiple nodes.…”

Section: Introductionmentioning

confidence: 99%

Active Learning-Based XGBoost for Cyber Physical System Against Generic AC False Data Injection Attacks

Xue

2020

IEEE Access

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With the exponential growth of information and communication technology, the traditional power system is gradually evolving into a cyber physical energy system (CPES) with frequently interactions between physical and cyber components. CPES indeed revolutionize the power grid efficiency and operational performance, but it also gives rise to new security challenges, causing catastrophic consequences in power system. Therefore, this paper proposes a new false data injection attack (FDIA) detection mechanism to automatically recognize the intrusions and thereby improve the cybersecurity of CPES. In this mechanism, a two-stage FDIA model is developed to generate training dataset for the machine learning-based detector, and an extreme gradient boosting (XGBoost) classifier is meticulously designed by combining with active learning and Bayesian optimization techniques to improve training efficiency and model performance respectively. The proposed FDIA defense strategy can properly extract the nonstationary and nonlinear attacking features and thus locate the biased states with a high accuracy. The obtained numerical results on the standard IEEE 14-, 57-and 118-bus systems highlight the destructive effects of the developed two-stage FDIA and comprehensively demonstrate the effectiveness of the proposed FDIA detection mechanism. INDEX TERMS Cyber physical energy system (CPES), false data injection attack (FDIA), extreme gradient boosting (XGBoost), FDIA defense strategy.

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“…LightGBM has a maximum depth parameter, it expands like a tree but prevents overfitting. Gradient boosting, due to its tree structure, is known to be good for tabular data but recently researchers have found it useful in a various applications [55][56][57][58][59][60][61][62][63][64][65][66][67].…”

Section: Why Lightgbm?mentioning

confidence: 99%

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

Gupta

Singh²,

Sinha³

2020

Preprint

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Voice Separation and Enhancement (VoSE) algorithm aims at designing a predictive model to solve the problem of speech enhancement and separation from a mixed signal. VoSE can be used for any language, with or without a large Datasets. VoSE can be utilized by any voice response system like, Siri, Alexa, Google Assistant which as of now work on single voice command. The pre-processing of the voice is done using a Trimming Negative and Nonzero voice filter (TNNVF), designed by the authors. TNNVF is independent of language, it works on any voice signal. The segmentation of a voice is generally carried out on frequency domain or time domain. Independently they are known to have ripple or rising effect. To rule out the ripple effect, data is filtered in the time-frequency domain. Voice print of the entire sound files is created for the training and testing purpose. 80% of the voice prints are used to train the network and 20% are kept for testing. The training set contains over 48,000 voice prints. LightGBM with TensorFlow helps in generating unique voice prints in a short time. To enhance the retrieved voice signals, Enhance Predictive Voice(EPV) function is designed. The tests are conducted on English and Indian languages. The proposed work is compared with K-means, Decision Stump, Naïve Bayes, and LSTM.

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Feature Recognition of Urban Road Traffic Accidents Based on GA-XGBoost in the Context of Big Data

Cited by 65 publications

References 28 publications

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

A Self-Care Prediction Model for Children with Disability Based on Genetic Algorithm and Extreme Gradient Boosting

Active Learning-Based XGBoost for Cyber Physical System Against Generic AC False Data Injection Attacks

VoSE: An algorithm to Separate and Enhance Voices from Mixed Signals using Gradient Boosting

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