| In the context of today’s data volume,data analysis and mining become particularly important.This technology processes data by combining artificial intelligence algorithms or mathematical models such as machine learning or deep learning,so that we can observe the status law and development trend of the data,and predict the probability of future events.Thus it can help us to formulate more reasonable and effective countermeasures.From the perspective of data analysis,this paper uses a variety of ensemble learning algorithms to conduct related research and experiments on PM2.5 prediction in meteorological data,demonstrating the significant advantage of the ensemble learning algorithm in the PM2.5 concentration prediction.The main content in this paper is as follows:(1)A Stacking algorithm based on feature weighting is proposed.The traditional Stacking algorithm generally uses equal weights in the cross-validation stage to combine the prediction results of each base learner.This method ignores the differences in prediction errors and contributions between different base learners,thereby affecting the generalization performance of the model.Therefore,this article uses the RMSE values of each base learner on the training set to weight the output results of each base learner in the first layer.This can give greater weight to base learners with good predictive performance,thereby improving the performance of the Stacking algorithm.(2)An improved Light GBM algorithm is proposed.In order to further improve the accuracy of the Light GBM algorithm in regression tasks,this paper improves the loss function of Light GBM.Specifically,this paper combines the Huber loss function with the Light GBM algorithm to obtain a new Light GBM algorithm,namely H-Light GBM.Because Huber loss function penalizes outlier more smoothly than MSE loss function does,H-Light GBM can better adapt to data with outlier and prevent overfitting.In addition,the Bayesian optimization method TPE(Tree structured Parzen Estimator)is used to optimize H-Light GBM,and the optimized TH-Light GBM model is used to predict PM2.5,which can further improve the accuracy of the prediction.Through experiments,the algorithm of this paper can overcome the poor prediction performance of a single model when the meteorological data has the characteristics of multi-dimensional,polyphonism,imbalance,and noise.In addition,the algorithm of this paper can reduce the differences between the models through the combination of multiple models,and produce more accurate and robust predictive effects in the training set and test set. |
