A personal credit forecasting method based on improved isolation random forest

2021 ◽  
Author(s):  
Rongxin Guo ◽  
Sihao Fu ◽  
Peilong Guan
Keyword(s):  
Random Forest ◽  
Personal Credit ◽  
Forecasting Method

scholarly journals Research on Random Forest Algorithm Based on Big Data in Parallel Load Forecasting

2018 ◽  
Vol 228 ◽  
pp. 01020 ◽  
Author(s):  
Qingqing Liu
Keyword(s):  
Random Forest ◽  
Decision Tree ◽  
Prediction Accuracy ◽  
Load Forecasting ◽  
Large Data ◽  
Prototype System ◽  
Data Sets ◽  
Random Forest Algorithm ◽  
Cluster Management ◽  
Forecasting Method

The paper propose a parallel load forecasting method based on random forest algorithm, through the analysis of historical load, temperature, wind speed and other data, the algorithm can shorten the load forecasting time and improve the processing capability of large data. This paper also designs and implements parallel load forecasting prototype system based on power user side large data of a Hadoop, including data cluster management, data management, prediction classification algorithm library and other functions. The experimental results show that the accuracy of parallel stochastic forest algorithm is obviously higher than decision tree, and the prediction accuracy on the different data sets is generally higher than decision tree, and it can better analyze and process large data.

Implementation of data mining as a support of business application strategy

2018 ◽  
Vol 5 (1) ◽  
pp. 47-55
Author(s):  
Florensia Unggul Damayanti
Keyword(s):  
Data Mining ◽  
Random Forest ◽  
Business Strategy ◽  
Input Parameter ◽  
Data Mining Algorithm ◽  
Complex Data ◽  
Business Decision ◽  
Marketing Department ◽  
Business Application ◽  
Complex Data Sets

Data mining help industries create intelligent decision on complex problems. Data mining algorithm can be applied to the data in order to forecasting, identity pattern, make rules and recommendations, analyze the sequence in complex data sets and retrieve fresh insights. Yet, increasing of technology and various techniques among data mining availability data give opportunity to industries to explore and gain valuable information from their data and use the information to support business decision making. This paper implement classification data mining in order to retrieve knowledge in customer databases to support marketing department while planning strategy for predict plan premium. The dataset decompose into conceptual analytic to identify characteristic data that can be used as input parameter of data mining model. Business decision and application is characterized by processing step, processing characteristic and processing outcome (Seng, J.L., Chen T.C. 2010). This paper set up experimental of data mining based on J48 and Random Forest classifiers and put a light on performance evaluation between J48 and random forest in the context of dataset in insurance industries. The experiment result are about classification accuracy and efficiency of J48 and Random Forest , also find out the most attribute that can be used to predict plan premium in context of strategic planning to support business strategy.

Database-Driven Modeling based on Variable Selection using Random Forest and Its Application for Linear Air Fuel Ratio Sensor Output Prediction

2019 ◽  
Vol 139 (8) ◽  
pp. 850-857
Author(s):  
Hiromu Imaji ◽  
Takuya Kinoshita ◽  
Toru Yamamoto ◽  
Keisuke Ito ◽  
Masahiro Yoshida ◽  
...  
Keyword(s):  
Random Forest ◽  
Variable Selection ◽  
Sensor Output ◽  
Fuel Ratio
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