Pose-based human action recognition with Extreme Gradient Boosting

Ayumi, Vina

doi:10.1109/scored.2016.7810099

Cited by 24 publications

(14 citation statements)

References 11 publications

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“…This is also consistent with other literatures which claim that XGBoost managed to achieve the best prediction when compared to other algorithms [31][32] [33]. The creator of XGBoost also reports that XGBoost was used by the top 10 winning teams in KDD Cup 2015 [34].…”

Section: Vresults and Discussionsupporting

confidence: 90%

Personality Prediction Based on Twitter Information in Bahasa Indonesia

Ong

Rahmanto

Williem

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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Abstract-The sheer usage of social media presents an opportunity for an automated analysis of a social media user based on his/her information, activities, or status updates. This opportunity is due to the abundant amount of information shared by the user. This fact is especially true for countries with high number of active social media users such as Indonesia. Extraction of information from social media can yield insightful results if done correctly. Recent studies have managed to leverage associations between language and personality and build a personality prediction system based on those associations. The current study attempts to build a personality prediction system based on a Twitter user's information for Bahasa Indonesia, the native language of Indonesia. The personality prediction system is built on Support Vector Machine and XGBoost trained with 329 instances (users). Evaluation results using 10-fold cross validation shows that the system managed to reach highest average accuracy of 76.2310% with Support Vector Machine and 97.9962% with XGBoost.

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Section: Vresults and Discussionsupporting

confidence: 90%

Personality Prediction Based on Twitter Information in Bahasa Indonesia

Ong

Rahmanto

Williem

et al. 2017

Proceedings of the 2017 Federated Conference on Computer Science and Information Systems

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“…XGB was first developed by [40], as an efficient, fast, and scalable implementation of the gradient tree boosting algorithm developed by [41]. XGB is classified as a supervised learning technique that uses an ensemble of decision trees [42]. It can be used for both classification and regression problems.…”

Section: B Extreme Gradient Boosting (Xgb)mentioning

confidence: 99%

The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow

et al. 2020

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Hybrid models that combine wavelet transformation (WT) as a pre-processing tool with datadriven models (DDMs) as modeling approaches have been widely investigated for forecasting streamflow. The WT approach has been applied to original time series for decomposing processes prior to the application of DDM modeling. This procedure has been applied to eliminate redundant patterns or information that lead to a dramatic increase in the model performance. In this study, three experiments were implemented, including stand-alone data-driven modeling, hind cast decomposing using WT divided and entered into the extreme learning machine (ELM), and the extreme gradient boosting (XGB) model to forecast streamflow data. The WT method was applied in two forms: discrete and continuous (DWT and CWT). In this paper, a new hybrid model is proposed based on an integrative prediction model where XGB is used as an input selection tool for the importance attributes of the prediction matrix that are then supplied to the ELM model as a predictive model. The monthly streamflow, upstream flow, rainfall, temperature, and potential evapotranspiration of a basin named in 1805 and located in the south east of Turkey, are used for development of the model. The modeling results show that applying the WT method improved the performance in the hindcast experiment based on the CWT form with minimum root mean square error (RMSE = 4.910 m 3 /s). On the contrary, WT deteriorated the performance of the forecasting and the stand-alone models exhibited a better performance. WT increased the performance of the hindcast experiment due to the inclusion of future information caused by convolution of the time series. However, the forecast experiment experienced deterioration due to the border effect at the end of the time series. Hence, WT was found not to be a useful pre-processing technique in forecasting the streamflow.

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“…Consequently, there was a need to enhance the results thereby resulted in the use of boosting. Boosting is a meta-algorithm used for the supervised learning style for primarily lessening bias and variance [18]. The algorithm transformation from strong to a weak learner is performed using a boosting technique.…”

Section: B Boostingmentioning

confidence: 99%

Machine Learning Based Malware Detection: A Boosting Methodology

Ghate*¹,

Pathade²,

Nirhali³

et al. 2020

IJITEE

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Malware damages computers without user's consent; they cause various threats unknowingly, hence detection of these is very crucial. In this study, we proposed to detect the presence of malware by using the classification technique of Machine Learning. Classification type in Machine Learning requires the output variable to be of a categorical kind; it attempts to draw some conclusion from the ascertained values. In short, classification constructs a model based on the training set and values or predicts categorical class labels. In our work, we propose to classify the presence of malware by incorporating two chief classification algorithms, such as Support Vector Machine and Logistic Regression. The data set used for it was not satisfactory. Consequently, we tend to explore a data set that met our necessities and enforced Logistic Regression on the same moreover, we plotted a scatter-gram for the scope of visualization and incorporated XG-Boost for the performance enhancement. This study assists in analyzing the presence of malware by adopting a proper dataset and ascertaining pivotal attributes leading to this classification.

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Pose-based human action recognition with Extreme Gradient Boosting

Cited by 24 publications

References 11 publications

Personality Prediction Based on Twitter Information in Bahasa Indonesia

Personality Prediction Based on Twitter Information in Bahasa Indonesia

The Capacity of the Hybridizing Wavelet Transformation Approach With Data-Driven Models for Modeling Monthly-Scale Streamflow

Machine Learning Based Malware Detection: A Boosting Methodology

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