基于集成學(xué)習(xí)的二次協(xié)同數(shù)據(jù)預(yù)測(cè)及優(yōu)化方法
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關(guān)鍵詞：Fancyimpute庫(kù)；數(shù)據(jù)插補(bǔ)；集成學(xué)習(xí)；BaggingRegressor模型；二次模型；協(xié)同預(yù)測(cè)模型中圖分類號(hào)：TP391.4 文獻(xiàn)標(biāo)識(shí)碼：A 文章編號(hào)：2096-4706（2025）07-0029-12
Abstract：The commonly used air quality prediction model has poor prediction efect on unknown conditions，and theactualmeteorologicalconditions haveasignificant impactontheconcentrationofairpollutants.Inorder toreduce the errorcausedbymeteorologicalconditionstothe model predictionof polutionconcentration，itisofgreat significance to obtain amodel with good prediction acuracy.Therefore，this paper proposes aquadraticcollaborative data prediction and optimization method basedon Ensemble Learning.Firstly，it combines the measured data with primary predicted data，and usestheFancyimpute libraryfordata interpolation for misingand deviating from the normal distribution data.Secondly，the BaggingRegressr model inEnsemble Learning is used toconstruct aquadratic model，adtheinfluence of meteorological conditions onpollutantconcentration isanalyzed fromthewhole tothe individual.Thevoting mechanism is usedtosynthesize allthe pedictionresults，andtheensemblepredictionresultsareobtained.Finally，acollborativedatapredictionmodelis constructed，andtelocationrelationshipndinddirectionfactorsareicludedforomprehensiveprediction.Theexprimetal results showthat themethodcanefectivelyimprovethepredictionaccuracyofthedataandthecolaborativepredictionmodel improves the prediction accuracy of the monitoring points.
Keywords：Fancyimpute library;data interpolation;Ensemble Learning; BaggingRegresor model;quadratic model; collaborative prediction model
0 引言
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