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Selective Gradient Boosting for Effective Learning to Rank

Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raffaele Perego, Salvatore Trani
2018 Zenodo  
In this paper, we propose Selective Gradient Boosting (SelGB), an algorithm addressing the Learning-to-Rank task by focusing on those irrelevant documents that are most likely to be mis-ranked, thus severely  ...  Learning an effective ranking function from a large number of query-document examples is a challenging task.  ...  Selective Gradient Boosting Unlike other algorithms discussed above, the query level samples produced by Selective Gradient Boosting are rank-aware.  ... 
doi:10.5281/zenodo.2668013 fatcat:tgoaimsigvc2lbqpp22w2tdx6q

Selective Gradient Boosting for Effective Learning to Rank

Claudio Lucchese, Franco Maria Nardini, Raffaele Perego, Salvatore Orlando, Salvatore Trani
2018 The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval - SIGIR '18  
In this paper, we propose Selective Gradient Boosting (SelGB), an algorithm addressing the Learning-to-Rank task by focusing on those irrelevant documents that are most likely to be mis-ranked, thus severely  ...  Learning an effective ranking function from a large number of query-document examples is a challenging task.  ...  Selective Gradient Boosting Unlike other algorithms discussed above, the query level samples produced by Selective Gradient Boosting are rank-aware.  ... 
doi:10.1145/3209978.3210048 dblp:conf/sigir/LuccheseN00T18 fatcat:6evdwifa3vf7phim4lyz7jsmk4

Interspecific Sample Prioritization Can Improve QTL Detection With Tree-Based Predictive Models

Min-Gyoung Shin, Sergey V. Nuzhdin
2021 Frontiers in Genetics  
We chose random forest and gradient boosting to apply the prioritization scheme and found that both facilitated the investigation of predictive causal markers in most of the biological scenarios simulated  ...  Due to increasing demand for new advanced crops, considerable efforts have been made to explore the improvement of stress and disease resistance cultivar traits through the study of wild crops.  ...  Among various machine learning approaches, random forest and gradient boosting methods are especially effective tree-based methods.  ... 
doi:10.3389/fgene.2021.684882 pmid:34552613 pmcid:PMC8450460 fatcat:ueordmvwlzf73klc5jos6cu244

Deep Similarity Learning for Sports Team Ranking [article]

Daniel Yazbek, Jonathan Sandile Sibindi, Terence L. Van Zyl
2021 arXiv   pre-print
Triplet loss produces the best overall results displaying the value of learning representations/embeddings for prediction and ranking of sports.  ...  In response, we focus on Siamese Neural Networks (SNN) in unison with LightGBM and XGBoost models, to predict the importance of matches and to rank teams in Rugby and Basketball.  ...  The reason for the combination is that the gradient boosting frameworks are effective at ranking tasks but limited in their ability to learn feature embeddings.  ... 
arXiv:2103.13736v1 fatcat:s4unaexv7fgtjplbopmk546eni

Cardiovascular Disease Prediction using Recursive Feature Elimination and Gradient Boosting Classification Techniques [article]

Prasannavenkatesan Theerthagiri, Vidya J
2021 arXiv   pre-print
This paper proposes a proposed recursive feature elimination-based gradient boosting (RFE-GB) algorithm in order to obtain accurate heart disease prediction.  ...  Machine learning algorithms are a promising method for identifying risk factors.  ...  It is an effective method for removing features from a training dataset in preparation for feature selection.  ... 
arXiv:2106.08889v1 fatcat:43z7t7x2g5dqnnte572pxlwgtu

Generalized BROOF-L2R

Clebson C.A. de Sá, Marcos A. Gonçalves, Daniel X. Sousa, Thiago Salles
2016 Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval - SIGIR '16  
The most effective learning methods for this task are based on ensembles of tress (e.g., Random Forests) and/or boosting techniques (e.g., Rank-Boost, MART, LambdaMART).  ...  In particular, we exploit out-of-bag samples as well as a selective weight updating strategy (according to the out-of-bag samples) to effectively enhance the ranking performance.  ...  Figure 2 : 2 Learning curve analysis for the boosting algorithms. Figure 3 : 3 BROOF gradient : Effect of out-of-bag samples versus entire training set.  ... 
doi:10.1145/2911451.2911540 dblp:conf/sigir/SaGSS16 fatcat:6iz6ljbn2vhx3cetroj4daymq4

Coupling feature selection and machine learning methods for navigational query identification

Yumao Lu, Fuchun Peng, Xin Li, Nawaaz Ahmed
2006 Proceedings of the 15th ACM international conference on Information and knowledge management - CIKM '06  
We find that gradient boosting tree, coupled with linear SVM feature selection is most effective. 3) With carefully coupled feature selection and classification approaches, navigational queries can be  ...  In this paper we study several machine learning methods, including naive Bayes model, maximum entropy model, support vector machine (SVM), and stochastic gradient boosting tree (SGBT), for navigational  ...  boosting tree) learning methods [19] to attack the problem.  ... 
doi:10.1145/1183614.1183711 dblp:conf/cikm/LuPLA06 fatcat:letgio3j6fhl3mvsqn6kr5eqka

Student Profile Modeling Using Boosting Algorithms

Touria Hamim, Faouzia Benabbou, Nawal Sael
2022 International Journal of Web-Based Learning and Teaching Technologies  
when using Information gain with Recursive Feature Elimination method compared to the other boosting algorithms.  ...  Machine learning plays an important role in this context and several studies have been carried out either for classification, prediction or clustering purpose.  ...  ranking methods for LightGBM prediction model.  ... 
doi:10.4018/ijwltt.20220901.oa4 fatcat:pi3o5h57bjatngjlyjv32xwv7e

A Comparative Analysis of XGBoost [article]

Candice Bentéjac and Anna Csörgő and Gonzalo Martínez-Muñoz
2019 arXiv   pre-print
XGBoost is a scalable ensemble technique based on gradient boosting that has demonstrated to be a reliable and efficient machine learning challenge solver.  ...  In addition, a comprehensive comparison between XGBoost, random forests and gradient boosting has been performed using carefully tuned models as well as using the default settings.  ...  For this experiment, the implementation of scikit-learn package [14] was used for random forest and gradient boosting. For XGBoost, the XGBoost package 1 was used.  ... 
arXiv:1911.01914v1 fatcat:p2cw6ygwkfbjfiwudp7ygkql44

A Novel Ensemble Framework for an Intelligent Intrusion Detection System

Sugandh Seth, Kuljit Kaur, Gurwinder Singh
2021 IEEE Access  
The F1-score of an algorithm is used to compute the rank matrix for different attack categories.  ...  Aims: To propose a unique ensemble framework that can effectively detect different attack categories.  ...  Since Histogram Based Gradient Boosting is the best ranked algorithm for detecting DDoS in the rank matrix, so its result is taken as the final output.  ... 
doi:10.1109/access.2021.3116219 fatcat:grvtzrpqfnccbawlxweixlyjmy

A Freeway Travel Time Prediction Method Based on An XGBoost Model

Zhen Chen, Wei Fan
2021 Sustainability  
., Gradient Boosting model). The comparison results indicate that the XGBoost model has considerable advantages in terms of both prediction accuracy and efficiency.  ...  In this study, an XGBoost model is employed to predict freeway travel time using probe vehicle data. The effects of different parameters on model performance are investigated and discussed.  ...  There are many boosting algorithms such as AdaBoost, Gradient boosting, and XGBoost. Gradient boosting is a typical boosting approach. It is widely used in the machine learning area.  ... 
doi:10.3390/su13158577 fatcat:xmtgvy5uujaennu4ox72kxny7a

Diagnosis of Tooth Prognosis Using Artificial Intelligence

Sang J. Lee, Dahee Chung, Akiko Asano, Daisuke Sasaki, Masahiko Maeno, Yoshiki Ishida, Takuya Kobayashi, Yukinori Kuwajima, John D. Da Silva, Shigemi Nagai
2022 Diagnostics  
Three AI machine-learning methods including gradient boosting classifier, decision tree classifier, and random forest classifier were used to create an algorithm.  ...  The objective of this study was to establish an effective artificial intelligence (AI)-based module for an accurate tooth prognosis decision based on the Harvard School of Dental Medicine (HSDM) comprehensive  ...  Acknowledgments: The authors acknowledge Jarshen Lin, Hiroe Ohyama, and Chia-Yu Chen for the input of their expertise on the selection of clinical determining factors for tooth prognosis, and Young Song  ... 
doi:10.3390/diagnostics12061422 pmid:35741232 pmcid:PMC9221626 fatcat:2zft6vq55renxc344m7yfktzra

InfiniteBoost: building infinite ensembles with gradient descent [article]

Alex Rogozhnikov, Tatiana Likhomanenko
2018 arXiv   pre-print
Two notable ensemble methods widely used in practice are gradient boosting and random forests.  ...  In machine learning ensemble methods have demonstrated high accuracy for the variety of problems in different areas.  ...  For gradient boosting shrinkage is varied to compare with InfiniteBoost.  ... 
arXiv:1706.01109v2 fatcat:z3kxlhjqcnhixayj52h7lm4gya

Gradient Regularized Budgeted Boosting [article]

Zhixiang Eddie Xu, Matt J. Kusner, Kilian Q. Weinberger, Alice X. Zheng
2019 arXiv   pre-print
Our model, based on gradient boosted regression trees (GBRT), is, to our knowledge, the first algorithm for semi-supervised budgeted learning.  ...  However, so far, these algorithms are limited to the supervised learning scenario where sufficient amounts of labeled data are available.  ...  In effect, the gradient propagates out from labeled inputs to unlabeled inputs.  ... 
arXiv:1901.04065v3 fatcat:xfb7g763nrfhthamdaisnjdxxi

Gradient Boosting Neural Networks: GrowNet [article]

Sarkhan Badirli, Xuanqing Liu, Zhengming Xing, Avradeep Bhowmik, Khoa Doan, Sathiya S. Keerthi
2020 arXiv   pre-print
General loss functions are considered under this unified framework with specific examples presented for classification, regression, and learning to rank.  ...  A fully corrective step is incorporated to remedy the pitfall of greedy function approximation of classic gradient boosting decision tree.  ...  visualization for the learning to rank task on MSLR dataset.  ... 
arXiv:2002.07971v2 fatcat:ck4smv5vrne7bf2f4crl7lxgei
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