Case Study II: Tuning of Gradient Boosting (xgboost)

Bartz-Beielstein, Thomas; Chandrasekaran, Sowmya; Rehbach, Frederik

doi:10.1007/978-981-19-5170-1_9

Cited by 4 publications

(1 citation statement)

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“…The depth of tree controls the depth of the individual trees. Smaller depth trees such as decision stumps are computationally efficient; however, higher depth trees allow the algorithm to capture unique interactions but also increase the risk of overfitting (Bartz-Beielstein et al , 2023). Minimum number of observations control complexity of each tree.…”

Section: Methodsmentioning

confidence: 99%

Machine learning approaches for prediction of ovarian cancer driver genes from mutational and network analysis

Wadapurkar

Bapat

Mahajan

et al. 2023

DTA

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PurposeOvarian cancer (OC) is the most common type of gynecologic cancer in the world with a high rate of mortality. Due to manifestation of generic symptoms and absence of specific biomarkers, OC is usually diagnosed at a late stage. Machine learning models can be employed to predict driver genes implicated in causative mutations.Design/methodology/approachIn the present study, a comprehensive next generation sequencing (NGS) analysis of whole exome sequences of 47 OC patients was carried out to identify clinically significant mutations. Nine functional features of 708 mutations identified were input into a machine learning classification model by employing the eXtreme Gradient Boosting (XGBoost) classifier method for prediction of OC driver genes.FindingsThe XGBoost classifier model yielded a classification accuracy of 0.946, which was superior to that obtained by other classifiers such as decision tree, Naive Bayes, random forest and support vector machine. Further, an interaction network was generated to identify and establish correlations with cancer-associated pathways and gene ontology data.Originality/valueThe final results revealed 12 putative candidate cancer driver genes, namely LAMA3, LAMC3, COL6A1, COL5A1, COL2A1, UGT1A1, BDNF, ANK1, WNT10A, FZD4, PLEKHG5 and CYP2C9, that may have implications in clinical diagnosis.

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Section: Methodsmentioning

confidence: 99%

Machine learning approaches for prediction of ovarian cancer driver genes from mutational and network analysis

Wadapurkar

Bapat

Mahajan

et al. 2023

DTA

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Multiclassification Analysis of Volumetric, Protocol, and Application Layer DDoS Attacks

Brown,

Fisher,

Hudon

et al. 2024

Lecture Notes on Data Engineering and Communications Technologies

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A novel bagging- XGBoost ensemble model for attaining high accuracy and computational efficiency in network intrusion detection

Nzuva,

Nder,

Mwalili

2024

E3S Web Conf.

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The study focuses on enhancing network intrusion detection to enhance network security and prevent potential data breaches. We propose B-XGBoost, an ensemble learning model that combines bagging and boosting, using 10k cross-validation and Bayesian optimization for binary network intrusion classification. The proposed model was trained and tested on the CIC-ID2017 dataset. Decision Trees, Random Forests, Support Vector Machines, Naive Bayes, k-Nearest Neighbors, and Neural Networks were trained and tested on the same dataset for performance comparison purposes. The results show that the BXGBoost algorithm had the highest F1 Score (0.982), Precision (0.975), Recall (0.990), Cohen’s Kappa (0.978), and ROC AUC (0.983). The other algorithms had varying levels of performance, with the Decision Trees having the second-highest F1 Score (0.950). Bayesian optimization significantly reduced the time, computational efficiency, and cost of hyperparameter tuning by using a probabilistic model to predict hyperparameters that resulted in high performance. The high scores in F1, precision, recall, agreement with human annotators, and ability to distinguish between positive and negative instances demonstrate the effectiveness of this approach in enhancing network security. For the best results of the B-XGBoost to be obtained, the hyperparameters of the base model need to be tuned to achieve maximum computational efficiency in light of the available resources.

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Case Study II: Tuning of Gradient Boosting (xgboost)

Cited by 4 publications

References 0 publications

Machine learning approaches for prediction of ovarian cancer driver genes from mutational and network analysis

Machine learning approaches for prediction of ovarian cancer driver genes from mutational and network analysis

Multiclassification Analysis of Volumetric, Protocol, and Application Layer DDoS Attacks

A novel bagging- XGBoost ensemble model for attaining high accuracy and computational efficiency in network intrusion detection

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