Author: Wu, Tingxian; Zhao, Ziru; Wei, Haoxiang; Peng, Yan
Title: Research on PM(2.5) Integrated Prediction Model Based on Lasso-RF-GAM Cord-id: xl0kqjgm Document date: 2020_7_11
ID: xl0kqjgm
Snippet: PM(2.5) concentration is very difficult to predict, for it is the result of complex interactions among various factors. This paper combines the random forest-recursive feature elimination algorithm and lasso regression for joint feature selection, puts forward a PM(2.5) concentration prediction model based on GAM. Firstly, the original data is standardized in the data input layer. Secondly, features were selected with RF-RFE and lasso regression algorithm in the feature selection layer. Meanwhil
Document: PM(2.5) concentration is very difficult to predict, for it is the result of complex interactions among various factors. This paper combines the random forest-recursive feature elimination algorithm and lasso regression for joint feature selection, puts forward a PM(2.5) concentration prediction model based on GAM. Firstly, the original data is standardized in the data input layer. Secondly, features were selected with RF-RFE and lasso regression algorithm in the feature selection layer. Meanwhile, weighted average method fused the two feature subsets to obtain the final subset, RF-lasso-T. Finally, the generalized additive models (GAM) is used to predict PM(2.5) concentration on the RF-lasso-T. Simulated experiments show that feature selection allows GAM model to run more efficiently. The deviance explained by the model reaches 91.5%, which is higher than only using a subset of RF-RFE. This model also reveals the influence of various factors on PM(2.5), which provides the decision-making basis for haze control.
Search related documents:
Co phrase search for related documents- absolute selection shrinkage operator and lung cancer: 1, 2, 3
- absolute selection shrinkage operator and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
- absolute selection shrinkage operator lasso method and logistic regression: 1, 2, 3, 4, 5
- absolute value and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9
- absolute value and machine learning: 1, 2, 3, 4, 5
- additive models and logistic regression: 1, 2, 3, 4, 5, 6, 7, 8, 9
- additive models and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9
- adjustment decision and machine learning: 1, 2
- logistic regression and loss function: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
- logistic regression and lung cancer: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52
- logistic regression and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74
- loss function and lung cancer: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
- loss function and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
- lung cancer and machine learning: 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
Co phrase search for related documents, hyperlinks ordered by date