KTBoost: Combined Kernel and Tree Boosting

Sigrist, Fabio

doi:10.1007/s11063-021-10434-9

Cited by 21 publications

(10 citation statements)

References 27 publications

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“…Classification is a type of supervised learning used to make predictions on categorical instances. In this research, we implemented six ML algorithms for predicting Anti-MRSA peptides: KTBoost, 36 SVM, 37 CatBoost, 38 Hist-GBoost, 39 CDF, 40 and XGBoost-RFC. 41 The implementation of all these classifiers was based on the Scikit-learn, 42 gcforest, 40 and KTBoost 43 packages.…”

Section: Methodsmentioning

confidence: 99%

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

Arif,

Fang,

Fida

et al. 2024

ACS Omega

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Methicillin-resistant Staphylococcus aureus (MRSA) is a growing concern for human lives worldwide. Anti-MRSA peptides act as potential antibiotic agents and play significant role to combat MRSA infection. Traditional laboratory-based methods for annotating Anti-MRSA peptides are although precise but quite challenging, costly, and time-consuming. Therefore, computational methods capable of identifying Anti-MRSA peptides accelerate the drug designing process for treating bacterial infections. In this study, we developed a novel sequence-based predictor "iM-RSAPred" for screening Anti-MRSA peptides by incorporating energy estimation and physiochemical and sequential information. We successfully resolved the skewed imbalance phenomena by using synthetic minority oversampling technique plus Tomek link (SMOTETomek) algorithm. Furthermore, the Shapley additive explanation method was leveraged to analyze the impact of topranked features in the prediction task. We evaluated multiple machine learning algorithms, i.e., CatBoost, Cascade Deep Forest, Kernel and Tree Boosting, support vector machine, and HistGBoost classifiers by 10-fold cross-validation and independent testing. The proposed iMRSAPred method significantly improved the overall performance in terms of accuracy and Matthew's correlation coefficient (MCC) by 5.45 and 0.083%, respectively, on the training data set. On the independent data set, iMRSAPred improved accuracy and MCC by 3.98 and 0.055%, respectively. We believe that the proposed method would be useful in large-scale Anti-MRSA peptide prediction and provide insights into other bioactive peptides.

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Section: Methodsmentioning

confidence: 99%

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

Arif,

Fang,

Fida

et al. 2024

ACS Omega

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“…Our dataset corresponds to clustered data where the dependency between the different inseminations competing for the fertilization of the same egg and the repeated use of ejaculates from the same male to inseminate different females have to be modeled while building the boosted trees. Therefore, we used the Gaussian Process Booster implemented in GPBoost python algorithm 58 . GPBoost uses LightGBM library 59 for tree learning and gradient descent for the covariance parameter learning.…”

Section: Methodsmentioning

confidence: 99%

Ranking parameters driving siring success during sperm competition in the North African houbara bustard

Sorci

Hussein

Levêque³

et al. 2023

Commun Biol

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Sperm competition is a powerful force driving the evolution of ejaculate and sperm traits. However, the outcome of sperm competition depends on many traits that extend beyond ejaculate quality. Here, we study male North African houbara bustards (Chlamydotis undulata undulata) competing for egg fertilization, after artificial insemination, with the aim to rank the importance of 14 parameters as drivers of siring success. Using a machine learning approach, we show that traits independent of male quality (i.e., insemination order, delay between insemination and egg laying) are the most important predictors of siring success. Traits describing intrinsic male quality (i.e., number of sperm in the ejaculate, mass motility index) are also positively associated with siring success, but their contribution to explaining the outcome of sperm competition is much lower than for insemination order. Overall, this analysis shows that males mating at the last position in the mating sequence have the best chance to win the competition for egg fertilization. This raises the question of the importance of female behavior as determinant of mating order.

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“…Despite the widespread usage of boosting algorithms, only one type of function is used as a base learner in most cases. In contrast to that, the KT‐Boost algorithm either adds a regression tree or a penalized reproducing kernel Hilbert space RKHS (kernel ridge regression function) to the ensemble of base classifiers in each boosting iteration [66]. In the beginning, the base learner is learned from both regression tree and RKHS function by employing gradient or newton as optimization techniques; afterward, the base learner whose inclusion in the ensemble results in the lower empirical risk is chosen.…”

Section: Proposed Methodologymentioning

confidence: 99%

Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel‐Tree boosting classifier—A novel sequentially executed supervised machine learning approach

Hussain

Mustafa

Al-Shqeerat

et al. 2022

IET Generation Trans & Dist

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This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel and Tree Boosting (KTBoost) classifier. It utilizes the intelligence of the XGBoost algorithm to estimate the missing values in the acquired dataset during the data pre-processing phase. An oversampling algorithm based on the Robust-SMOTE technique is utilized to avoid the unbalanced data class distribution issue. Afterward, with the aid of few very significant statistical, temporal, and spectral features extracted from the acquired kWh dataset, the complex underlying data patterns are comprehended to enhance the accuracy and detection rate of the classifier. For effectively classifying the consumers into "Honest" and "Fraudster," the ensemble machine learning-based classifier KTBoost, with Jaya algorithm optimized hyperparameters, is utilized. Finally, the developed model is re-trained using a reduced set of highly important features to minimize the computational resources without compromising the performance of the developed model. The outcome of this study reveals that the proposed theft detection method achieves the highest accuracy (93.38%), precision (95%), and recall (93.18%) among all the studied methods, thus signifying its importance in the studied area of research.

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KTBoost: Combined Kernel and Tree Boosting

Cited by 21 publications

References 27 publications

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

iMRSAPred: Improved Prediction of Anti-MRSA Peptides Using Physicochemical and Pairwise Contact-Energy Properties of Amino Acids

Ranking parameters driving siring success during sperm competition in the North African houbara bustard

Electric theft detection in advanced metering infrastructure using Jaya optimized combined Kernel‐Tree boosting classifier—A novel sequentially executed supervised machine learning approach

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