scholarly journals A Combination Method of the Tanimoto Coefficient and Proximity Measure of Random Forest for Compound Activity Prediction

2008 ◽  
Vol 4 ◽  
pp. 238-249
Author(s):  
Gen Kawamura ◽  
Shigeto Seno ◽  
Yoichi Takenaka ◽  
Hideo Matsuda
Keyword(s):  
Random Forest ◽  
Combination Method ◽  
Proximity Measure ◽  
Activity Prediction ◽  
Tanimoto Coefficient

scholarly journals EASY ENSEMMBLE WITH RANDOM FOREST TO HANDLE IMBALANCED DATA IN CLASSIFICATION

2020 ◽  
Vol 3 (1) ◽  
pp. 39-46 ◽  
Author(s):  
Sarini Abdullah ◽  
GV Prasetyo
Keyword(s):  
Random Forest ◽  
Data Science ◽  
Credit Scoring ◽  
Model Performance ◽  
Imbalanced Data ◽  
Problem Definition ◽  
Combination Method ◽  
Data Set ◽  
Minority Class ◽  
Imbalance Problem

Imbalanced data might cause some issues in problem definition level, algorithm level, and data level. Some of the methods have been developed to overcome this issue, one of state-of-the-art method is Easy Ensemble. Easy Ensemble was claimed can improve model performance to classify minority class, and overcome the deficiency of random under- sampling. In this paper we discussed the implementation of Easy Ensemble with Random Forest Classifiers to handle imbalance problem in credit scoring case. This combination method is implemented in two datasets which taken from data science competition website, finhacks.id and kaggle.com with class proportion within majority and minority is 70:30 and 94:6. The results showed that resampling with Easy Ensemble can improve Random Forest classifier performance upon minority class. Recall on minority class increased significantly after the resampling. Before resampling, the recall on minority class for the first dataset (finhacks.id) was 0.49, and increased to 0.82 after the resampling. Similar results were obtained for the second data set (kaggle.com), where the recall for the minority class was increased from just 0.14 to 0.73.

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

Sentinel Node Navigation Surgery with Combination Method with Dye and Radioisotopes for Malignant Melanoma

2006 ◽  
Vol 68 (3) ◽  
pp. 274-279 ◽  
Author(s):  
Akira TAKAHASHI ◽  
Naoya YAMAZAKI ◽  
Akifumi YAMAMOTO ◽  
Kouji YOSHINO ◽  
Kenjiro NAMIKAWA ◽  
...  
Keyword(s):  
Malignant Melanoma ◽  
Sentinel Node ◽  
Combination Method ◽  
Navigation Surgery ◽  
Sentinel Node Navigation Surgery

Random Forest: A Review

2017 ◽  
Vol 7 (1) ◽  
pp. 251-257 ◽  
Author(s):  
Eesha Goel ◽  
◽  
Er. Abhilasha ◽  
Keyword(s):  
Random Forest
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