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Prediction of purchasing power of Google store based on deep ensemble learning model

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DOI: 10.23977/autml.2019.11001 | Downloads: 8 | Views: 886

Author(s)

Wang Peng 1

Affiliation(s)

1 School of Economics and Management, Dalian University, No.10, Xuefu Avenue, Economic & Technical Development Zone, Dalian, Liaoning,The People's Republic of China(PRC)

Corresponding Author

Wang Peng

ABSTRACT

Aiming at the defect that the ensemble learning model such as Light Gradient Boosting Machine only mines the data information once, which can not automatically refine the granularity of data mining and dig into the more potential internal correlation information of data, the ensemble learning model is made into a deep form by sliding window and deepening, and the deep ensemble learning is proposed. Sliding window enables the ensemble learning model to automatically refine the granularity of data mining, so as to dig deeper into the potential internal correlation information in the data, and at the same time endue it with certain representation learning ability. Based on the sliding window, the deepening step further improves the representation learning ability of the model. Finally, the results show that the prediction accuracy of the deep ensemble learning model is 6.16 percentage points higher than that of the original ensemble learning model.

KEYWORDS

machine learning; data mining; deep model; ensemble learning; feature engineering

CITE THIS PAPER

Wang Peng, Prediction of purchasing power of Google store based on deep ensemble learning model . Automation and Machine Learning(2019) Vol. 1: 1-4. DOI: http://dx.doi.org/10.23977/autml.2019.11001.

REFERENCES

[1] Pouria Sadeghi-Tehran, (2017) Multi-feature machine learning model for automatic segmentation of green fractional vegetation cover for high-throughput field phenotyping, Plant methods, 1,96-108
[2] Sung Kyun Park. (2015) Construction of environmental risk score beyond standard linear models using machine learning methods: application to metal mixtures, oxidative stress and cardiovascular disease in NHANES, Environmental Health, 1, 356-374.
[3] Tanchanok Wisitponchai. (2018) AnkPlex: algorithmic structure for refinement of near-native ankyrin-protein docking. BMC Bioinformatics, 2, 119-136.

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