https://oldena.lpnu.ua/handle/ntb/52503| Title: | Using Stacking Approaches for Machine Learning Models |
| Authors: | Pavlyshenko, Bohdan |
| Affiliation: | Ivan Franko National University of Lviv |
| Bibliographic description (Ukraine): | Pavlyshenko B. Using Stacking Approaches for Machine Learning Models / Bohdan Pavlyshenko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 255–258. — (Dynamic Data Mining & Data Stream Mining). |
| Bibliographic description (International): | Pavlyshenko B. Using Stacking Approaches for Machine Learning Models / Bohdan Pavlyshenko // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 255–258. — (Dynamic Data Mining & Data Stream Mining). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | machine learning stacking forecasting classification regression |
| Number of pages: | 4 |
| Page range: | 255-258 |
| Start page: | 255 |
| End page: | 258 |
| Abstract: | In this paper, we study the usage of stacking approach for building ensembles of machine learning models. The cases for time series forecasting and logistic regression have been considered. The results show that using stacking technics we can improve performance of predictive models in considered cases. |
| URI: | https://ena.lpnu.ua/handle/ntb/52503 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| URL for reference material: | http://kaggle.com http://www.kaggle.com/c/rossmann-store-sales https://www.kaggle.com/c/grupo-bimbo-inventory-demand https://www.kaggle.com/c/grupo-bimbo-inventorydemand/discussion/23863 https://www.kaggle.com/bpavlyshenko/bimboxgboost-r-script-lb-0-457 https://www.kaggle.com/c/bosch-production-line-performance https://www.kaggle.com/c/boschproduction-line-performance/forums/t/24065/the-magical-feature-fromlb-0-3-to-0-4 https://www.kaggle.com/mmueller/bosch-production-lineperformance/road-2-0-4 https://www.kaggle.com/alexxanderlarko/boschproduction-line-performance/road-2-0-4-featureset http://sourceforge.net/projects/mcmcjags/files/Manuals/3.x/jags |
| References (Ukraine): | [1] Kaggle: Your Home for Data Science. URL: http://kaggle.com [2] B. M. Pavlyshenko. “Linear, machine learning and probabilistic approaches for time series analysis,” in IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, pp. 377-381, August 23-27, 2016. [3] “Rossmann Store Sales”, Kaggle.Com, URL: http://www.kaggle.com/c/rossmann-store-sales . [4] D. H. Wolpert. “Stacked generalization.” Neural networks, 5(2), pp. 241-259,1992. [5] Kaggle competition “Grupo Bimbo Inventory Demand ” URL: https://www.kaggle.com/c/grupo-bimbo-inventory-demand [6] Kaggle competition “Grupo Bimbo Inventory Demand” URL:https://www.kaggle.com/c/grupo-bimbo-inventorydemand/discussion/23863 [7] T. Chen and C. Guestrin. “Xgboost: A scalable tree boosting system.” In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM. 2016, pp. 785-794. [8] Kaggle competition “Grupo Bimbo Inventory Demand” Bimbo XGBoost R script LB:0.457. URL: https://www.kaggle.com/bpavlyshenko/bimboxgboost-r-script-lb-0-457 [9] J. Friedman. “Greedy function approximation: a gradient boosting machine.”, Annals of Statistics, 29(5):1189-1232, 2001. [10] J. Friedman. “Stochastic gradient boosting.”, Computational Statistics & Data Analysis, 38(4):367-378, 2002. [11] Kaggle competition “Bosch Production Line Performance”. URL: https://www.kaggle.com/c/bosch-production-line-performance [12] B. Pavlyshenko. “Machine learning, linear and bayesian models for logistic regression in failure detection problems.,’ in IEEE International Conference on Big Data (Big Data), Washington D.C., USA, pp. 2046-2050, December 5-8, 2016. [13] Kaggle competition “Bosch Production Line Performance”. The Magical Feature : from LB 0.3- to 0.4+. URL:https://www.kaggle.com/c/boschproduction-line-performance/forums/t/24065/the-magical-feature-fromlb-0-3-to-0-4 [14] Kaggle competition “Bosch Production Line Performance”. Road2-0.4+. URL:https://www.kaggle.com/mmueller/bosch-production-lineperformance/road-2-0-4 [15] Kaggle competition “Bosch Production Line Performance”. Road-2-0.4+ –>FeatureSet++. URL: https://www.kaggle.com/alexxanderlarko/boschproduction-line-performance/road-2-0-4-featureset [16] John Kruschke. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press, 2014. [17] Martyn Plummer. JAGS Version 3.4.0 user manual. URL:http://sourceforge.net/projects/mcmcjags/files/Manuals/3.x/jags user manual.pdf |
| References (International): | [1] Kaggle: Your Home for Data Science. URL: http://kaggle.com [2] B. M. Pavlyshenko. "Linear, machine learning and probabilistic approaches for time series analysis," in IEEE First International Conference on Data Stream Mining & Processing (DSMP), Lviv, Ukraine, pp. 377-381, August 23-27, 2016. [3] "Rossmann Store Sales", Kaggle.Com, URL: http://www.kaggle.com/c/rossmann-store-sales . [4] D. H. Wolpert. "Stacked generalization." Neural networks, 5(2), pp. 241-259,1992. [5] Kaggle competition "Grupo Bimbo Inventory Demand " URL: https://www.kaggle.com/c/grupo-bimbo-inventory-demand [6] Kaggle competition "Grupo Bimbo Inventory Demand" URL:https://www.kaggle.com/c/grupo-bimbo-inventorydemand/discussion/23863 [7] T. Chen and C. Guestrin. "Xgboost: A scalable tree boosting system." In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM. 2016, pp. 785-794. [8] Kaggle competition "Grupo Bimbo Inventory Demand" Bimbo XGBoost R script LB:0.457. URL: https://www.kaggle.com/bpavlyshenko/bimboxgboost-r-script-lb-0-457 [9] J. Friedman. "Greedy function approximation: a gradient boosting machine.", Annals of Statistics, 29(5):1189-1232, 2001. [10] J. Friedman. "Stochastic gradient boosting.", Computational Statistics & Data Analysis, 38(4):367-378, 2002. [11] Kaggle competition "Bosch Production Line Performance". URL: https://www.kaggle.com/c/bosch-production-line-performance [12] B. Pavlyshenko. "Machine learning, linear and bayesian models for logistic regression in failure detection problems.,’ in IEEE International Conference on Big Data (Big Data), Washington D.C., USA, pp. 2046-2050, December 5-8, 2016. [13] Kaggle competition "Bosch Production Line Performance". The Magical Feature : from LB 0.3- to 0.4+. URL:https://www.kaggle.com/c/boschproduction-line-performance/forums/t/24065/the-magical-feature-fromlb-0-3-to-0-4 [14] Kaggle competition "Bosch Production Line Performance". Road2-0.4+. URL:https://www.kaggle.com/mmueller/bosch-production-lineperformance/road-2-0-4 [15] Kaggle competition "Bosch Production Line Performance". Road-2-0.4+ –>FeatureSet++. URL: https://www.kaggle.com/alexxanderlarko/boschproduction-line-performance/road-2-0-4-featureset [16] John Kruschke. Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press, 2014. [17] Martyn Plummer. JAGS Version 3.4.0 user manual. URL:http://sourceforge.net/projects/mcmcjags/files/Manuals/3.x/jags user manual.pdf |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Pavlyshenko_B-Using_Stacking_Approaches_255-258.pdf | 239.3 kB | Adobe PDF | View/Open | |
| 2018_Pavlyshenko_B-Using_Stacking_Approaches_255-258__COVER.png | 549.8 kB | image/png | View/Open |
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