Hardware Implementation of Random Forest Algorithm Based on Classification and Regression Tree

To cope with the increasing charging demand of electric vehicle (EV), this paper presents a forecasting method of EV charging load based on random forest algorithm (RF) and the load data of a single charging station. This method is completed by the classification and regression tree (CART) algorithm to realize short-term forecast for the station. At the same time, the prediction algorithm of the daily charging capacity of charging stations with different scales and locations is proposed. By combining the regression and classification algorithms, the effective learning of a large amount of historical charging data is completed. The characteristic data is divided from different aspects, realizing the establishment of RF and the effective prediction of fluctuate charging load. By analyzing the data of each charging station in Shenzhen from the aspect of time and space, the algorithm is put into practice. The application form of current data in the algorithm is determined, and the accuracy of the prediction algorithm is verified to be reliable and practical. It can provide a reference for both power suppliers and users through the prediction of charging load.

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Erratum to: Landslide susceptibility mapping using random forest, boosted regression tree, classification and regression tree, and general linear models and comparison of their performance at Wadi Tayyah Basin, Asir Region, Saudi Arabia

Landslides ◽

10.1007/s10346-015-0667-1 ◽

2015 ◽

Vol 13 (5) ◽

pp. 1315-1318 ◽

Cited By ~ 9

Author(s):

Ahmed Mohamed Youssef ◽

Hamid Reza Pourghasemi ◽

Zohre Sadat Pourtaghi ◽

Mohamed M. Al-Katheeri

Keyword(s):

Saudi Arabia ◽

Random Forest ◽

Landslide Susceptibility ◽

Linear Models ◽

Regression Tree ◽

Classification And Regression Tree ◽

Landslide Susceptibility Mapping ◽

Boosted Regression Tree ◽

Classification And Regression ◽

Asir Region

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Penerapan Metode Machine Learning dalam Klasifikasi Risiko Kejadian Berat Badan Lahir Rendah di Indonesia

Matrik Jurnal Manajemen Teknik Informatika dan Rekayasa Komputer ◽

10.30812/matrik.v20i2.1174 ◽

2021 ◽

Vol 20 (2) ◽

pp. 417-426

Author(s):

Pardomuan Robinson Sihombing ◽

Istiqomatul Fajriyah Yuliati

Keyword(s):

Machine Learning ◽

Support Vector Machine ◽

Random Forest ◽

Naive Bayes ◽

Regression Tree ◽

Imbalanced Data ◽

Classification And Regression Tree ◽

Support Vector ◽

Classification And Regression ◽

Sensitivity Specificity

Penelitian ini akan mengkaji penerapan beberapa metode machine learning dengan memperhatikan kasus imbalanced data dalam pemodelan klasifikasi untuk penentuan risiko kejadian bayi dengan BBLR yang diharapkan dapat menjadi solusi dalam menurunkan kelahiran bayi dengan BBLR di Indonesia. Adapun metode meachine learning yang digunakan adalah Classification and Regression Tree (CART), Naïve Bayes, Random Forest dan Support Vector Machine (SVM). Pemodelan klasifikasi dengan menggunakan teknik resample pada kasus imbalanced data dan set data besar terbukti mampu meningkatkan ketepatan klasifikasi khususnya terhadap kelas minoritas yang dapat diihat dari nilai sensitivity yang tinggi dibandingkan data asli (tanpa treatment). Selanjutnya, dari kelima model klasifikasi yang iuji menunjukkan bahwa model random forest memberikan kinerja terbaik berdasarkan nilai sensitivity, specificity, G-mean dan AUC tertinggi. Variabel terpenting/paling berpengaruh dalam klasifikasi resiko kejadian BBLR adalah jarak dan urutan kelahiran, pemeriksaan kehamilan, dan umur ibu

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